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title: "Minlie Huang — papers, page 2 — Elamur Research"
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# Minlie Huang · page 2

31 papers in our library · 2016–2026 · page 2 of 2

[The profile, affiliations and co-authors →](https://md.research.elamur.ai/free/en/authors/minlie-huang/)

## Papers

The most cited first (OpenAlex and our library).

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26. **[Crisp: Cognitive Restructuring of Negative Thoughts through Multi-turn Supportive Dialogues](https://md.research.elamur.ai/free/en/papers/zhou-2025-crisp-cognitive-restructuring-negative-thoughts/)** *(2025 · 10 citations)* Cognitive Restructuring (CR) is a therapy technique in which a counselor helps someone uncover distorted negative thoughts and reshape them into more balanced ones through conversation.
27. **[How Should We Enhance the Safety of Large Reasoning Models: An Empirical Study](https://md.research.elamur.ai/free/en/papers/zhang-2026-how-should-we-enhance-safety/)** *(2026 · 6 citations)* Large Reasoning Models (LRMs) are strong at maths and code, but their safety often stays the same or gets worse.
28. **[IF-CRITIC: Towards a Fine-Grained LLM Critic for Instruction-Following Evaluation](https://md.research.elamur.ai/free/en/papers/wen-2026-if-critic-fine-grained-llm/)** *(2026 · 1 citation)* LLM-as-a-Judge is often used to supply rewards for training instruction-following. It has two problems. First, it is expensive, because strong proprietary or reasoning models must judge every constraint in a separate call. Second, it is unreliable: these judges miss errors and count poorly. IF-CRITIC is a 14B critic…
29. **[IF-RewardBench: Benchmarking Judge Models for Instruction-Following Evaluation](https://md.research.elamur.ai/free/en/papers/wen-2026-if-rewardbench-benchmarking-judge-models/)** *(2026 · 1 citation)* Judge models (LLM-as-a-Judge and reward models) are widely used to check whether LLM outputs obey instructions and to supply reward signals for alignment.
30. **[Data Efficient RLVR via Off-Policy Influence Guidance](https://md.research.elamur.ai/free/en/papers/zhu-2026-data-efficient-rlvr-off-policy/)** *(2026)* Choosing training data for reinforcement learning with verifiable rewards (RLVR) usually relies on heuristics such as difficulty or uncertainty.
31. **[LASA: Language-Agnostic Semantic Alignment at the Semantic Bottleneck for LLM Safety](https://md.research.elamur.ai/free/en/papers/yang-2026-lasa-language-agnostic-semantic-alignment/)** *(2026)* LLMs tend to refuse harmful requests reliably in high-resource languages but are much easier to jailbreak in low-resource ones.

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