Paper-Conference

Aligning Large Language Models with Human Opinions through Persona Selection and Value-Belief-Norm Reasoning
Chain-of-Opinion selects and reasons over explicit and implicit personae to improve LLM opinion prediction and alignment.
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Aligning Large Language Models with Human Opinions through Persona Selection and Value-Belief-Norm Reasoning
DnA-Eval: Enhancing Large Language Model Evaluation through Decomposition and Aggregation
DnA-Eval decomposes and aggregates LLM-as-judge evaluation to improve interpretability and agreement with human preferences.
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DnA-Eval: Enhancing Large Language Model Evaluation through Decomposition and Aggregation
Monte Carlo Tree Search Boosts Reasoning via Iterative Preference Learning
A NeurIPS 2024 workshop paper on boosting reasoning with MCTS-driven iterative preference learning.
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Multi-expert Prompting Improves Reliability, Safety and Usefulness of Large Language Models
An EMNLP 2024 prompting method that simulates and aggregates multiple expert responses to improve LLM reliability, safety, and usefulness.
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Multi-expert Prompting Improves Reliability, Safety and Usefulness of Large Language Models
Advancing Adversarial Suffix Transfer Learning on Aligned Large Language Models
An EMNLP 2024 study of adversarial suffix transfer learning on aligned large language models.
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DataTales: A Benchmark for Real-World Intelligent Data Narration
An EMNLP 2024 benchmark of 4.9k financial reports and market data for evaluating data narration by language models.
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DataTales: A Benchmark for Real-World Intelligent Data Narration
MVP-Bench: Can Large Vision-Language Models Conduct Multi-level Visual Perception Like Humans?
A Findings of EMNLP 2024 benchmark for multi-level visual perception in large vision-language models.
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Reasoning Robustness of LLMs to Adversarial Typographical Errors
An EMNLP 2024 study showing that adversarial typos can substantially degrade LLM reasoning and introducing the R2ATA robustness benchmark.
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Reasoning Robustness of LLMs to Adversarial Typographical Errors
V-DPO: Mitigating Hallucination in Large Vision Language Models via Vision-Guided Direct Preference Optimization
A Findings of EMNLP 2024 method for mitigating hallucination in large vision language models.
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Beyond Memorization: The Challenge of Random Memory Access in Language Models
we investigate whether a generative LM (e.g., GPT- 2) is able to access its memory sequentially or randomly. Through carefully-designed synthetic tasks, we reveal that LMs manage to sequentially access their memory while encountering challenges in randomly accessing memorized content.
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Beyond Memorization: The Challenge of Random Memory Access in Language Models