--- title: "Caiming Xiong — papers, page 2 — Elamur Research" description: "Caiming Xiong, Salesforce: 38 papers on Elamur Research (2016–2026), h-index 68 — affiliations, topics, co-authors and who cites them in…" canonical: "https://research.elamur.ai/free/en/authors/caiming-xiong/page/2/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/caiming-xiong/page/2/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / [Caiming Xiong](https://md.research.elamur.ai/free/en/authors/caiming-xiong/) / page 2 # Caiming Xiong · page 2 38 papers in our library · 2016–2026 · page 2 of 2 [The profile, affiliations and co-authors →](https://md.research.elamur.ai/free/en/authors/caiming-xiong/) ## Papers The most cited first (OpenAlex and our library). [← prev](https://md.research.elamur.ai/free/en/authors/caiming-xiong/) [1](https://md.research.elamur.ai/free/en/authors/caiming-xiong/) **2** 26. **[DialFact: A Benchmark for Fact-Checking in Dialogue](https://md.research.elamur.ai/free/en/papers/gupta-2022-dialfact-benchmark-fact-checking-dialogue/)** *(2022 · 97 citations)* Automatic fact-checking has mostly focused on formal text such as news, Wikipedia sentences or tables. Conversational utterances are harder: they are informal, mix opinions with facts, and are full of ellipsis and coreference, yet no public benchmark existed for them. The authors frame dialogue fact-checking as three… 27. **[Find or Classify? Dual Strategy for Slot-Value Predictions on Multi-Domain Dialog State Tracking](https://md.research.elamur.ai/free/en/papers/zhang-2020-find-or-classify-dual-strategy/)** *(2020 · 79 citations)* Dialog state tracking (DST) in task-oriented assistants must infer, turn by turn, every \ triplet a user wants. 28. **[Summary of a Haystack: A Challenge to Long-Context LLMs and RAG Systems](https://md.research.elamur.ai/free/en/papers/laban-2024-summary-haystack-challenge-long-context/)** *(2024 · 69 citations)* Long-context LLMs and RAG pipelines can now take in enormous inputs, but simple probes such as Needle-in-a-Haystack are too easy to separate strong systems. 29. **[Direct Judgement Preference Optimization](https://md.research.elamur.ai/free/en/papers/wang-2025-direct-judgement-preference-optimization/)** *(2025 · 35 citations)* LLM-as-judge models are usually trained with supervised finetuning on fairly small datasets and cover only narrow evaluation formats, often just pairwise comparison. 30. **[CRMArena: Understanding the Capacity of LLM Agents to Perform Professional CRM Tasks in Realistic Environments](https://md.research.elamur.ai/free/en/papers/huang-2025-crmarena-understanding-capacity-llm-agents/)** *(2025 · 30 citations)* Testing LLM agents on real enterprise work is hard. Company CRM data is private, and existing work benchmarks such as WorkArena, WorkBench and Tau-Bench use simple data structures and easy tasks. The authors built CRMArena, a benchmark set inside a real Salesforce organization. With help from CRM practitioners, they… 31. **[L2CEval: Evaluating Language-to-Code Generation Capabilities of Large Language Models](https://md.research.elamur.ai/free/en/papers/ni-2024-l2ceval-evaluating-language-code-generation/)** *(2024 · 27 citations)* Large language models trained on code can turn natural-language requests into executable programs, but earlier evaluations covered isolated tasks, single model families or particular learning setups. 32. **[CodeTree: Agent-guided Tree Search for Code Generation with Large Language Models](https://md.research.elamur.ai/free/en/papers/li-2025-codetree-agent-guided-tree-search/)** *(2025 · 21 citations)* Hard programming tasks have a huge space of possible solutions. Older methods either sample many independent candidates or repeatedly refine one early attempt, which can trap them in a local optimum. CodeTree addresses this by organizing code generation as a tree search run by cooperating LLM agents. A Thinker agent… 33. **[DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AI](https://md.research.elamur.ai/free/en/papers/zhang-2023-dialogstudio-towards-richest-most-diverse/)** *(2023 · 21 citations)* Conversational models often struggle across varied dialogue tasks because training data is fragmented, small, task-specific and stored in inconsistent formats. 34. **[Shared Imagination: LLMs Hallucinate Alike](https://md.research.elamur.ai/free/en/papers/zhou-2024-shared-imagination-llms-hallucinate-alike/)** *(2024 · 7 citations)* Modern LLMs are trained with very similar recipes, so the authors ask whether the resulting models are also similar in what they invent. 35. **[Simple Data Augmentation with the Mask Token Improves Domain Adaptation for Dialog Act Tagging](https://md.research.elamur.ai/free/en/papers/yavuz-2020-simple-data-augmentation-mask-token/)** *(2020 · 5 citations)* Dialog act (DA) taggers learn speaker intentions such as "request" or "inform". These intentions are shared across task-oriented domains, yet taggers trained on one domain transfer poorly to another, and labeling new domains is costly. The authors adapt DA taggers using only unlabeled target-domain dialogues. Their… 36. **[J4R: Learning to Judge with Equivalent Initial State Group Relative Policy Optimization](https://md.research.elamur.ai/free/en/papers/xu-2026-j4r-learning-judge-equivalent-initial/)** *(2026 · 3 citations)* LLM-as-judge models handle simple tasks like chat quality well, but they struggle to evaluate reasoning-heavy outputs. 37. **[Hard2Verify: A Step-Level Verification Benchmark for Open-Ended Frontier Math](https://md.research.elamur.ai/free/en/papers/pandit-2026-hard2verify-step-level-verification-benchmark/)** *(2026 · 2 citations)* Frontier LLMs now write Olympiad-level proofs, such as the gold-medal results at IMO 2025. Training and evaluating such systems requires automatic verifiers that can judge each step of a proof, not just the final answer. Existing step-level benchmarks have weaknesses: they use easy questions, outputs from weak… 38. **[From Passive Metric to Active Signal: The Evolving Role of Uncertainty Quantification in Large Language Models](https://md.research.elamur.ai/free/en/papers/zhang-2026-passive-metric-active-signal-evolving/)** *(2026 · 1 citation)* Most uncertainty quantification (UQ) work on LLMs scores an output only after it has been generated. This survey argues that such retrospective scoring does not fit multi-step reasoning, agents or alignment loops. It traces a shift in which uncertainty becomes a live control input that changes what the model does… [← prev](https://md.research.elamur.ai/free/en/authors/caiming-xiong/) [1](https://md.research.elamur.ai/free/en/authors/caiming-xiong/) **2**