---
title: "Who cites Rishi Bommasani — page 2"
description: "2,113 papers of the Elamur Research library cite the works of Rishi Bommasani, found in their bibliographies · page 2 of 2 · 101–196; the most cited first."
canonical: "https://research.elamur.ai/free/en/authors/rishi-bommasani/cited-by/page/2/"
---
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# Who cites Rishi Bommasani

2,198 citations of their works from 2,113 papers of our library (of the 140,884 whose bibliographies we read) · 196 with a page here, the most cited first · page 2 of 2 · 101–196 · [← Rishi Bommasani](https://md.research.elamur.ai/free/en/authors/rishi-bommasani/)

[← prev](https://md.research.elamur.ai/free/en/authors/rishi-bommasani/cited-by/) [1](https://md.research.elamur.ai/free/en/authors/rishi-bommasani/cited-by/) **2**

101. [**LexGLUE: A Benchmark Dataset for Legal Language Understanding in English**Ilias Chalkidis; Abhik Jana; Dirk Hartung; Michael Bommarito; Ion Androutsopoulos; Daniel Martin Katz;, Nikolaos Aletras*2021 · 256 citations*](https://md.research.elamur.ai/free/en/papers/chalkidis-2021-lexglue-benchmark-dataset-legal-language/)
102. [**Scaling Relationship on Learning Mathematical Reasoning with Large Language Models**Zheng Yuan, Hongyi Yuan, Chengpeng Li, et al.*2023 · 252 citations*](https://md.research.elamur.ai/free/en/papers/yuan-2023-scaling-relationship-learning-mathematical-reasoning/)
103. [**Why Can GPT Learn In-Context? Language Models Secretly Perform Gradient Descent as Meta-Optimizers**Damai Dai, Yutao Sun, Li Dong, et al.*2023 · 252 citations*](https://md.research.elamur.ai/free/en/papers/dai-2023-why-can-gpt-learn-context/)
104. [**Personality Traits in Large Language Models**Greg Serapio-Garcia, Mustafa Safdari, Clement Crepy, et al.*2023 · 251 citations*](https://md.research.elamur.ai/free/en/papers/serapio-garcia-2023-personality-traits-large-language-models/)
105. [**The Curse of Recursion: Training on Generated Data Makes Models Forget**Ilia Shumailov, Zakhar Shumaylov, Yiren Zhao, et al.*2023 · 250 citations*](https://md.research.elamur.ai/free/en/papers/shumailov-2023-curse-recursion-training-generated-data/)
106. [**DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models**Boxin Wang, Weixin Chen, Hengzhi Pei, et al.*2023 · 246 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-decodingtrust-comprehensive-assessment-trustworthiness-gpt/)
107. [**Tree of Attacks: Jailbreaking Black-Box LLMs Automatically**Anay Mehrotra, Manolis Zampetakis, Paul Kassianik, et al.*2023 · 245 citations*](https://md.research.elamur.ai/free/en/papers/mehrotra-2023-tree-attacks-jailbreaking-black-box/)
108. [**Factuality Challenges in the Era of Large Language Models**Isabelle Augenstein, Timothy Baldwin, Meeyoung Cha, et al.*2023 · 241 citations*](https://md.research.elamur.ai/free/en/papers/augenstein-2023-factuality-challenges-era-large-language/)
109. [**RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment**Hanze Dong, Wei Xiong, Deepanshu Goyal, et al.*2023 · 239 citations*](https://md.research.elamur.ai/free/en/papers/dong-2023-raft-reward-ranked-finetuning-generative/)
110. [**BlenderBot 3: a deployed conversational agent that continually learns to responsibly engage**Kurt Shuster, Jing Xu, Mojtaba Komeili, et al.*2022 · 234 citations*](https://md.research.elamur.ai/free/en/papers/shuster-2022-blenderbot-3-deployed-conversational-agent/)
111. [**ChatGPT-4 Outperforms Experts and Crowd Workers in Annotating Political Twitter Messages with Zero-Shot Learning**Petter Tornberg*2023 · 230 citations*](https://md.research.elamur.ai/free/en/papers/tornberg-2023-chatgpt-4-outperforms-experts-crowd/)
112. [**TrustLLM: Trustworthiness in Large Language Models**Yue Huang, Lichao Sun, Haoran Wang, et al.*2024 · 226 citations*](https://md.research.elamur.ai/free/en/papers/huang-2024-trustllm-trustworthiness-large-language-models/)
113. [**Not All Languages Are Created Equal in LLMs: Improving Multilingual Capability by Cross-Lingual-Thought Prompting**Haoyang Huang, Tianyi Tang, Dongdong Zhang, et al.*2023 · 223 citations*](https://md.research.elamur.ai/free/en/papers/huang-2023-all-languages-created-equal-llms/)
114. [**Specializing Smaller Language Models towards Multi-Step Reasoning**Yao Fu, Hao Peng, Litu Ou, et al.*2023 · 222 citations*](https://md.research.elamur.ai/free/en/papers/fu-2023-specializing-smaller-language-models-towards/)
115. [**A Survey of Hallucination in Large Foundation Models**Vipula Rawte, Amit Sheth, Amitava Das*2023 · 221 citations*](https://md.research.elamur.ai/free/en/papers/rawte-2023-survey-hallucination-large-foundation-models/)
116. [**Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection**Kai Greshake, Sahar Abdelnabi, Shailesh Mishra, et al.*2023 · 220 citations*](https://md.research.elamur.ai/free/en/papers/greshake-2023-ve-signed-up-compromising-real/)
117. [**Beyond neural scaling laws: beating power law scaling via data pruning**Ben Sorscher, Robert Geirhos, Shashank Shekhar, et al.*2022 · 220 citations*](https://md.research.elamur.ai/free/en/papers/sorscher-2022-neural-scaling-laws-beating-power/)
118. [**Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment**Yang Liu; Yuanshun Yao; Jean-Francois Ton; Xiaoying Zhang; Ruocheng Guo; Hao Cheng; Yegor Klochkov; Muhammad Faaiz Taufiq;, Hang Li*2023 · 219 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-trustworthy-llms-survey-guideline-evaluating/)
119. [**Self-critiquing models for assisting human evaluators**William Saunders, Catherine Yeh, Jeff Wu, et al.*2022 · 219 citations*](https://md.research.elamur.ai/free/en/papers/saunders-2022-self-critiquing-models-assisting-human/)
120. [**FOLIO: Natural Language Reasoning with First-Order Logic**Simeng Han, Hailey Schoelkopf, Yilun Zhao, et al.*2022 · 217 citations*](https://md.research.elamur.ai/free/en/papers/han-2022-folio-natural-language-reasoning-first/)
121. [**Distilling Reasoning Capabilities into Smaller Language Models**Kumar Shridhar, Alessandro Stolfo, Mrinmaya Sachan*2023 · 216 citations*](https://md.research.elamur.ai/free/en/papers/shridhar-2023-distilling-reasoning-capabilities-into-smaller/)
122. [**MADLAD-400: A Multilingual And Document-Level Large Audited Dataset**Sneha Kudugunta, Isaac Caswell, Biao Zhang, et al.*2023 · 211 citations*](https://md.research.elamur.ai/free/en/papers/kudugunta-2023-madlad-400-multilingual-document-level/)
123. [**Progress measures for grokking via mechanistic interpretability**Neel Nanda, Lawrence Chan, Tom Lieberum, et al.*2023 · 207 citations*](https://md.research.elamur.ai/free/en/papers/nanda-2023-progress-measures-grokking-mechanistic-interpretability/)
124. [**Active Example Selection for In-Context Learning**Yiming Zhang, Shi Feng, Chenhao Tan*2022 · 207 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2022-active-example-selection-context-learning/)
125. [**Prompting GPT-3 To Be Reliable**Chenglei Si, Zhe Gan, Zhengyuan Yang, et al.*2022 · 206 citations*](https://md.research.elamur.ai/free/en/papers/si-2022-prompting-gpt-3-reliable/)
126. [**SALMONN: Towards Generic Hearing Abilities for Large Language Models**Changli Tang, Wenyi Yu, Guangzhi Sun, et al.*2023 · 202 citations*](https://md.research.elamur.ai/free/en/papers/tang-2023-salmonn-towards-generic-hearing-abilities/)
127. [**A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasets**Md Tahmid Rahman Laskar, M Saiful Bari, Mizanur Rahman, et al.*2023 · 198 citations*](https://md.research.elamur.ai/free/en/papers/laskar-2023-systematic-study-comprehensive-evaluation-chatgpt/)
128. [**Towards Faithful Model Explanation in NLP: A Survey**Qing Lyu, Marianna Apidianaki, Chris Callison-Burch*2024 · 194 citations*](https://md.research.elamur.ai/free/en/papers/lyu-2024-faithful-model-explanation-nlp-survey/)
129. [**Okapi: Instruction-tuned Large Language Models in Multiple Languages with Reinforcement Learning from Human Feedback**Viet Lai, Chien Nguyen, Nghia Ngo, et al.*2023 · 194 citations*](https://md.research.elamur.ai/free/en/papers/lai-2023-okapi-instruction-tuned-large-language/)
130. [**Towards Making the Most of ChatGPT for Machine Translation**Keqin Peng, Liang Ding, Qihuang Zhong, et al.*2023 · 190 citations*](https://md.research.elamur.ai/free/en/papers/peng-2023-making-most-chatgpt-machine-translation/)
131. [**The Prompt Report: A Systematic Survey of Prompt Engineering Techniques**Sander Schulhoff, Michael Ilie, Nishant Balepur, et al.*2024 · 187 citations*](https://md.research.elamur.ai/free/en/papers/schulhoff-2024-prompt-report-systematic-survey-prompt/)
132. [**SGPT: GPT Sentence Embeddings for Semantic Search**Niklas Muennighoff*2022 · 187 citations*](https://md.research.elamur.ai/free/en/papers/muennighoff-2022-sgpt-gpt-sentence-embeddings-semantic/)
133. [**CulturaX: A Cleaned, Enormous, and Multilingual Dataset for Large Language Models in 167 Languages**Thuat Nguyen, Chien Van Nguyen, Viet Dac Lai, et al.*2023 · 179 citations*](https://md.research.elamur.ai/free/en/papers/nguyen-2023-culturax-cleaned-enormous-multilingual-dataset/)
134. [**Debiasing Pre-trained Contextualised Embeddings**Masahiro Kaneko, Danushka Bollegala*2021 · 174 citations*](https://md.research.elamur.ai/free/en/papers/kaneko-2021-debiasing-pre-trained-contextualised-embeddings/)
135. [**Promptagator: Few-shot Dense Retrieval From 8 Examples**Zhuyun Dai, Vincent Y. Zhao, Ji Ma, et al.*2022 · 167 citations*](https://md.research.elamur.ai/free/en/papers/dai-2022-promptagator-few-shot-dense-retrieval/)
136. [**Improve Mathematical Reasoning in Language Models by Automated Process Supervision**Liangchen Luo, Yinxiao Liu, Rosanne Liu, et al.*2024 · 163 citations*](https://md.research.elamur.ai/free/en/papers/luo-2024-improve-mathematical-reasoning-language-models/)
137. [**Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision**Collin Burns, Pavel Izmailov, Jan Hendrik Kirchner, et al.*2023 · 163 citations*](https://md.research.elamur.ai/free/en/papers/burns-2023-weak-strong-generalization-eliciting-strong/)
138. [**Orca 2: Teaching Small Language Models How to Reason**Arindam Mitra, Luciano Del Corro, Shweti Mahajan, et al.*2023 · 160 citations*](https://md.research.elamur.ai/free/en/papers/mitra-2023-orca-2-teaching-small-language/)
139. [**Reasoning with Language Model Prompting: A Survey**Shuofei Qiao, Yixin Ou, Ningyu Zhang, et al.*2022 · 160 citations*](https://md.research.elamur.ai/free/en/papers/qiao-2022-reasoning-language-model-prompting-survey/)
140. [**GPT-4 Is Too Smart To Be Safe: Stealthy Chat with LLMs via Cipher**Youliang Yuan, Wenxiang Jiao, Wenxuan Wang, et al.*2023 · 158 citations*](https://md.research.elamur.ai/free/en/papers/yuan-2023-gpt-4-too-smart-safe/)
141. [**TheoremQA: A Theorem-driven Question Answering Dataset**Wenhu Chen, Ming Yin, Max Ku, et al.*2023 · 154 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-theoremqa-theorem-driven-question-answering/)
142. [**Leveraging Large Language Models for Multiple Choice Question Answering**Joshua Robinson, Christopher Michael Rytting, David Wingate*2022 · 154 citations*](https://md.research.elamur.ai/free/en/papers/robinson-2022-leveraging-large-language-models-multiple/)
143. [**Draft & Verify: Lossless Large Language Model Acceleration via Self-Speculative Decoding**Jun Zhang, Jue Wang, Huan Li, et al.*2024 · 146 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2024-draft-verify-lossless-large-language/)
144. [**SCOTT: Self-Consistent Chain-of-Thought Distillation**Peifeng Wang, Zhengyang Wang, Zheng Li, et al.*2023 · 145 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-scott-self-consistent-chain-thought/)
145. [**Self-Evaluation Guided Beam Search for Reasoning**Yuxi Xie, Kenji Kawaguchi, Yiran Zhao, et al.*2023 · 145 citations*](https://md.research.elamur.ai/free/en/papers/xie-2023-self-evaluation-guided-beam-search/)
146. [**MiniLLM: On-Policy Distillation of Large Language Models**Yuxian Gu, Li Dong, Furu Wei, et al.*2023 · 143 citations*](https://md.research.elamur.ai/free/en/papers/gu-2023-minillm-policy-distillation-large-language/)
147. [**How does GPT-2 compute greater-than?: Interpreting mathematical abilities in a pre-trained language model**Michael Hanna, Ollie Liu, Alexandre Variengien*2023 · 140 citations*](https://md.research.elamur.ai/free/en/papers/hanna-2023-gpt-2-compute-greater-than/)
148. [**DeepInception: Hypnotize Large Language Model to Be Jailbreaker**Xuan Li, Zhanke Zhou, Jianing Zhu, et al.*2023 · 138 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-deepinception-hypnotize-large-language-model/)
149. [**A Roadmap to Pluralistic Alignment**Taylor Sorensen, Jared Moore, Jillian Fisher, et al.*2024 · 135 citations*](https://md.research.elamur.ai/free/en/papers/sorensen-2024-roadmap-pluralistic-alignment/)
150. [**Task-aware Retrieval with Instructions**Akari Asai, Timo Schick, Patrick Lewis, et al.*2023 · 131 citations*](https://md.research.elamur.ai/free/en/papers/asai-2023-task-aware-retrieval-instructions/)
151. [**Secrets of RLHF in Large Language Models Part I: PPO**Rui Zheng, Shihan Dou, Songyang Gao, et al.*2023 · 130 citations*](https://md.research.elamur.ai/free/en/papers/zheng-2023-secrets-rlhf-large-language-models/)
152. [**Improving Language Model Negotiation with Self-Play and In-Context Learning from AI Feedback**Yao Fu, Hao Peng, Tushar Khot, et al.*2023 · 129 citations*](https://md.research.elamur.ai/free/en/papers/fu-2023-improving-language-model-negotiation-self/)
153. [**Retrieval meets Long Context Large Language Models**Peng Xu, Wei Ping, Xianchao Wu, et al.*2023 · 129 citations*](https://md.research.elamur.ai/free/en/papers/xu-2023-retrieval-meets-long-context-large/)
154. [**Deductive Verification of Chain-of-Thought Reasoning**Zhan Ling, Yunhao Fang, Xuanlin Li, et al.*2023 · 128 citations*](https://md.research.elamur.ai/free/en/papers/ling-2023-deductive-verification-chain-thought-reasoning/)
155. [**InfLLM: Training-Free Long-Context Extrapolation for LLMs with an Efficient Context Memory**Chaojun Xiao, Pengle Zhang, Xu Han, et al.*2024 · 125 citations*](https://md.research.elamur.ai/free/en/papers/xiao-2024-infllm-training-free-long-context/)
156. [**Safety-Tuned LLaMAs: Lessons From Improving the Safety of Large Language Models that Follow Instructions**Federico Bianchi, Mirac Suzgun, Giuseppe Attanasio, et al.*2023 · 124 citations*](https://md.research.elamur.ai/free/en/papers/bianchi-2023-safety-tuned-llamas-lessons-improving/)
157. [**From Persona to Personalization: A Survey on Role-Playing Language Agents**Jiangjie Chen, Xintao Wang, Rui Xu, et al.*2024 · 114 citations*](https://md.research.elamur.ai/free/en/papers/chen-2024-persona-personalization-survey-role-playing/)
158. [**Quiet-STaR: Language models can teach themselves to think before speaking***2024 · 113 citations*](https://md.research.elamur.ai/free/en/papers/al-2024-quiet-star-language-models-teach/)
159. [**Olmo 3**Team Olmo: Allyson Ettinger, Amanda Bertsch, Bailey Kuehl, et al.*2025 · 109 citations*](https://md.research.elamur.ai/free/en/papers/ettinger-2025-olmo-3/)
160. [**RestGPT: Connecting Large Language Models with Real-World RESTful APIs**Yifan Song, Weimin Xiong, Dawei Zhu, et al.*2023 · 108 citations*](https://md.research.elamur.ai/free/en/papers/song-2023-restgpt-connecting-large-language-models/)
161. [**ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models**Jinheon Baek, Sujay Kumar Jauhar, Silviu Cucerzan, et al.*2024 · 106 citations*](https://md.research.elamur.ai/free/en/papers/baek-2024-researchagent-iterative-research-idea/)
162. [**Inverse Scaling: When Bigger Isn't Better**Ian R. McKenzie, Alexander Lyzhov, Michael Pieler, et al.*2023 · 99 citations*](https://md.research.elamur.ai/free/en/papers/mckenzie-2023-inverse-scaling-bigger-isn-t/)
163. [**Language Modeling Is Compression**Gregoire Deletang, Anian Ruoss, Paul-Ambroise Duquenne, et al.*2023 · 89 citations*](https://md.research.elamur.ai/free/en/papers/deletang-2023-language-modeling-compression/)
164. [**Bridging the Gap: A Survey on Integrating (Human) Feedback for Natural Language Generation**Patrick Fernandes, Aman Madaan, Emmy Liu, et al.*2023 · 84 citations*](https://md.research.elamur.ai/free/en/papers/fernandes-2023-bridging-gap-survey-integrating-human/)
165. [**Transcending Scaling Laws with 0.1% Extra Compute**Yi Tay, Jason Wei, Hyung Won Chung, et al.*2022 · 40 citations*](https://md.research.elamur.ai/free/en/papers/tay-2022-transcending-scaling-laws-0-1/)
166. [**Towards Effective Disambiguation for Machine Translation with Large Language Models**Vivek Iyer, Pinzhen Chen, Alexandra Birch*2023 · 38 citations*](https://md.research.elamur.ai/free/en/papers/iyer-2023-effective-disambiguation-machine-translation-large/)
167. [**OG-RAG: Ontology-grounded retrieval-augmented generation for large language models**Kartik Sharma, Peeyush Kumar, Yunqing Li*2025 · 37 citations*](https://md.research.elamur.ai/free/en/papers/sharma-2025-og-rag-ontology-grounded-retrieval/)
168. [**Pragmatics in Language Grounding: Phenomena, Tasks, and Modeling Approaches**Daniel Fried, Nicholas Tomlin, Jennifer Hu, et al.*2023 · 36 citations*](https://md.research.elamur.ai/free/en/papers/fried-2023-pragmatics-language-grounding-phenomena-tasks/)
169. [**Observational Scaling Laws and the Predictability of Language Model Performance**Yangjun Ruan, Chris J. Maddison, Tatsunori Hashimoto*2024 · 28 citations*](https://md.research.elamur.ai/free/en/papers/ruan-2024-observational-scaling-laws-predictability-language/)
170. [**Taqyim: Evaluating Arabic NLP Tasks Using ChatGPT Models**Zaid Alyafeai, Maged S. Alshaibani, Badr AlKhamissi, et al.*2023 · 25 citations*](https://md.research.elamur.ai/free/en/papers/alyafeai-2023-taqyim-evaluating-arabic-nlp-tasks/)
171. [**Scaling Laws and Compute-Optimal Training Beyond Fixed Training Durations**Alexander Hägele, Elie Bakouch, Atli Kosson, et al.*2024 · 24 citations*](https://md.research.elamur.ai/free/en/papers/hagele-2024-scaling-laws-compute-optimal-training/)
172. [**DADA: Dialect Adaptation via Dynamic Aggregation of Linguistic Rules**Yanchen Liu, William Held, Diyi Yang*2023 · 20 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-dada-dialect-adaptation-dynamic-aggregation/)
173. [**Evaluating statistical language models as pragmatic reasoners**Benjamin Lipkin, Lionel Wong, Gabriel Grand, et al.*2023 · 18 citations*](https://md.research.elamur.ai/free/en/papers/lipkin-2023-evaluating-statistical-language-models-pragmatic/)
174. [**Skywork-Math: Data Scaling Laws for Mathematical Reasoning in Large Language Models — The Story Goes On**Liang Zeng, Liangjun Zhong, Liang Zhao, et al.*2024 · 14 citations*](https://md.research.elamur.ai/free/en/papers/zeng-2024-skywork-math-data-scaling-laws/)
175. [**Unveiling Language Competence Neurons: A Psycholinguistic Approach to Model Interpretability**Xufeng Duan, Xinyu Zhou, Bei Xiao, et al.*2025 · 8 citations*](https://md.research.elamur.ai/free/en/papers/duan-2025-unveiling-language-competence-neurons-psycholinguistic/)
176. [**Bridging the Resource Gap: Exploring the Efficacy of English and Multilingual LLMs for Swedish**Oskar Holmström, Jenny Kunz, Marco Kuhlmann*2023 · 8 citations*](https://md.research.elamur.ai/free/en/papers/holmstrom-2023-bridging-resource-gap-exploring-efficacy/)
177. [**MedAraBench: Large-Scale Arabic Medical Question Answering Dataset and Benchmark**Mouath Abu-Daoud, Leen Kharouf, Omar El Hajj, et al.*2026 · 5 citations*](https://md.research.elamur.ai/free/en/papers/abu-daoud-2026-medarabench-large-scale-arabic-medical/)
178. [**Gender Disambiguation in Machine Translation: Diagnostic Evaluation in Decoder-Only Architectures**Chiara Manna, Hosein Mohebbi, Afra Alishahi, et al.*2026 · 3 citations*](https://md.research.elamur.ai/free/en/papers/manna-2026-gender-disambiguation-machine-translation-diagnostic/)
179. [**Benchmarking Large Language Models for Chinese and Japanese IMEs: Phonetic-to-Character Generation and Textual Error Correction**Yuchun Zou, Tedd Lee, Xiaodi Fan, et al.*2026 · 2 citations*](https://md.research.elamur.ai/free/en/papers/zou-2026-benchmarking-large-language-models-chinese/)
180. [**Fine-Tashkeel: Finetuning Byte-Level Models for Accurate Arabic Text Diacritization**Bashar Al-Rfooh, Gheith Abandah, Rami Al-Rfou*2023 · 2 citations*](https://md.research.elamur.ai/free/en/papers/al-rfooh-2023-fine-tashkeel-finetuning-byte-level/)
181. [**Construction Grammar Evidence for How LLMs Use Context-Directed Extrapolation to Solve Tasks**Harish Tayyar Madabushi, Claire Bonial*2025 · 1 citation*](https://md.research.elamur.ai/free/en/papers/madabushi-2025-construction-grammar-evidence-how-llms/)
182. [**A Survey on Parameter-efficient Fine-tuning of Large Models: Techniques, Trends, and Challenges (大模型参数高效微调方法综述: 技术、趋势与挑战)**Anda Tang (唐岸达), Zhouchen Lin (林宙辰)*2026*](https://md.research.elamur.ai/free/en/papers/tang-2026-parameter-efficient-fine/)
183. [**AmazoniaNLP: A Survey of Extreme Low-Resource Languages in the Peruvian-Brazilian Amazon**Rodolfo Joel Zevallos, Fabrício Carraro, John E. Ortega*2026*](https://md.research.elamur.ai/free/en/papers/zevallos-2026-amazonianlp-survey-extreme-low-resource/)
184. [**From sunblock to softblock: Analyzing the correlates of neology in published writing and on social media**Maria Ryskina, Matthew R. Gormley, Kyle Mahowald, et al.*2026*](https://md.research.elamur.ai/free/en/papers/ryskina-2026-sunblock-softblock-analyzing-correlates-neology/)
185. [**Headlines You Won't Forget: Can Pronoun Insertion Increase Memorability?**Selina Meyer, Magdalena Abel, Michael Roth*2026*](https://md.research.elamur.ai/free/en/papers/meyer-2026-headlines-you-won-t-forget/)
186. [**How Far Can Bias Go? Tracing Bias from Pre-Training Data to Alignment**Marion Thaler, Abdullatif Köksal, Alina Leidinger, et al.*2026*](https://md.research.elamur.ai/free/en/papers/thaler-2026-how-far-can-bias-go/)
187. [**Is Biomedical Specialization Still Worth It? Insights from Domain-Adaptive Language Modelling with a New French Health Corpus**Aidan Mannion, Cécile Macaire, Armand Violle, et al.*2026*](https://md.research.elamur.ai/free/en/papers/mannion-2026-is-biomedical-specialization-still-worth/)
188. [**Lost in Translation: Repurposing semantic similarity benchmarks for evaluating lexical-semantic consistency in LLM-based machine translation**Quin Ye, Jelke Bloem*2026*](https://md.research.elamur.ai/free/en/papers/ye-2026-lost-translation-repurposing-semantic-similarity/)
189. [**On Temperature-Constrained Non-Deterministic Machine Translation: Potential and Evaluation**Weichuan Wang, Mingyang Liu, Chen Ma, et al.*2026*](https://md.research.elamur.ai/free/en/papers/wang-2026-temperature-constrained-non-deterministic-machine/)
190. [**One Word Is Not Enough: Simple Prompts Improve Word Embeddings**Rajeev Ranjan*2026*](https://md.research.elamur.ai/free/en/papers/ranjan-2026-one-word-is-not-enough/)
191. [**Recent Advances in Text-attributed Graph Learning via Large Models (大模型驱动的文本属性图学习研究进展)**Yanping Zheng (郑艳萍), Jiarui Ji (季嘉蕊), Yuwei Hu (胡雨韦), et al.*2026*](https://md.research.elamur.ai/free/en/papers/zheng-2026-recent-advances-text/)
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