Papers by Yongjin Yang
Agent-to-Agent Theory of Mind: Testing Interlocutor Awareness among Large Language Models (2025.emnlp-main)
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| Challenge: | Prior work has focused on situational awareness, which refers to a model's ability to recognize its operating phase and constraints, but it has neglected the complementary capacity to identify and adapt to the identity and characteristics of a dialogue partner. |
| Approach: | They formalize interlocutor awareness and evaluate its emergence in contemporary LLMs. |
| Outcome: | The proposed model reliably identify same-family peers and certain prominent model families, such as GPT and Claude. |
Code-Switching Red-Teaming: LLM Evaluation for Safety and Multilingual Understanding (2025.acl-long)
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| Challenge: | Recent large language models (LLMs) are inherently multilingual agents . concerns regarding their safety have emerged . |
| Approach: | They propose a framework to synthesize red-teaming queries and investigate their safety . they demonstrate that the framework outperforms existing red- teaming techniques . |
| Outcome: | The proposed framework outperforms existing red-teaming techniques in the safety domain . it generates code-switching attack prompts in monolingual data . |
MAQA: Evaluating Uncertainty Quantification in LLMs Regarding Data Uncertainty (2025.findings-naacl)
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| Challenge: | despite advances in large language models, they still produce false but incorrect responses. |
| Approach: | They propose a new benchmark for large language models that requires more than two unambiguous answers . they also assess 5 different uncertainty quantification methods in the presence of data uncertainty. |
| Outcome: | The proposed method fails in multi-answer question answering tasks compared to single-answered questions . entropy- and consistency-based methods effectively estimate model uncertainty, the authors show . |
Self-Training Elicits Concise Reasoning in Large Language Models (2025.findings-acl)
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| Challenge: | Chain-of-thought reasoning has enabled large language models to use additional computation through intermediate tokens to solve complex tasks, but current models often generate more tokens than necessary to accomplish the task, incurring extraneous inference costs. |
| Approach: | They propose to fine-tune models with self-generated concise reasoning paths obtained by best-of-N sampling and few-shot conditioning in task-specific settings to elicit concise reasoning. |
| Outcome: | The proposed method reduces output tokens by 30% on GSM8K and MATH while maintaining average accuracy. |
CORE: Measuring Multi-Agent LLM Interaction Quality under Game-Theoretic Pressures (2026.eacl-long)
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| Challenge: | Game-theoretic interactions between agents with large language models (LLMs) have revealed many emergent capabilities, yet the linguistic diversity of these interactions has not been quantified. |
| Approach: | They propose a metric to quantify the effectiveness of language use within multi-agent systems across different game-theoretic interactions. |
| Outcome: | The proposed metric measures the effectiveness of language use within multi-agent systems across game-theoretic interactions. |
HARE: Explainable Hate Speech Detection with Step-by-Step Reasoning (2023.findings-emnlp)
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| Challenge: | Recent benchmarks have attempted to identify and explain hate speech but lack the reasoning to supervise detection models. |
| Approach: | They propose a framework that uses large language models to fill in the gaps in hate speech explanations by using existing annotations. |
| Outcome: | The proposed framework outperforms baselines on SBIC and Implicit Hate using model-generated data and improves generalization to unseen datasets. |
Towards Difficulty-Agnostic Efficient Transfer Learning for Vision-Language Models (2024.emnlp-main)
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| Challenge: | Vision-language models (VLMs) have demonstrated remarkable applicability across downstream tasks, including zero-shot image classification. |
| Approach: | They propose an efficient transfer learning method that integrates visual prompts and text adapters with pre-trained VLMs to achieve optimal performance for any target domain. |
| Outcome: | The proposed method outperforms baselines on unseen tasks. |