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Bosi Wen

9 accepted papers

2026

RLMR: Reinforcement Learning with Mixed Rewards for Creative Writing

AAAI 2026technical

Large language models are extensively utilized in creative writing applications. Creative writing requires a balance between subjective writing quality (e.g., literariness and emotional expression) and objective constraint following (e.g., format requirements and word limits). Existing reinforcement

Cited by 0SourcePDFScholar
2025

CharacterBench: Benchmarking Character Customization of Large Language Models

AAAI 2025technical

Character-based dialogue (aka role-playing) enables users to freely customize characters for interaction, which often relies on LLMs, raising the need to evaluate LLMs’ character customization capability. However, existing benchmarks fail to ensure a robust evaluation as they often only involve a si…

2025

HPSS: Heuristic Prompting Strategy Search for LLM Evaluators

ACL 2025finding

Since the adoption of large language models (LLMs) for text evaluation has become increasingly prevalent in the field of natural language processing (NLP), a series of existing works attempt to optimize the prompts for LLM evaluators to improve their alignment with human judgment. However, their eff…

2025

Training Language Model to Critique for Better Refinement

ACL 2025finding

Large language models (LLMs) have demonstrated remarkable evaluation and critique capabilities, providing insightful feedback and identifying flaws in various tasks. However, limited research has explored which types of critiques are most effective for improving model responses or how to generate su…

2024

AlignBench: Benchmarking Chinese Alignment of Large Language Models

ACL 2024long

Alignment has become a critical step for instruction-tuned Large Language Models (LLMs) to become helpful assistants. However, effective evaluation of alignment for emerging Chinese LLMs is still significantly lacking, calling for real-scenario grounded, open-ended, challenging and automatic evaluat…

2024

Benchmarking Complex Instruction-Following with Multiple Constraints Composition

NeurIPS 2024poster

Instruction following is one of the fundamental capabilities of large language models (LLMs). As the ability of LLMs is constantly improving, they have been increasingly applied to deal with complex human instructions in real-world scenarios. Therefore, how to evaluate the ability of complex instruc…

2024

CharacterGLM: Customizing Social Characters with Large Language Models

EMNLP 2024industry

Character-based dialogue (CharacterDial) has become essential in the industry (e.g., Character.AI), enabling users to freely customize social characters for social interactions. However, the generalizability and adaptability across various conversational scenarios inherent in customizing social char…

Cited by 0SourcePDFScholar
2024

CritiqueLLM: Towards an Informative Critique Generation Model for Evaluation of Large Language Model Generation

ACL 2024long

Since the natural language processing (NLP) community started to make large language models (LLMs) act as a critic to evaluate the quality of generated texts, most of the existing works train a critique generation model on the evaluation data labeled by GPT-4’s direct prompting. We observe that thes…

2024

ToMBench: Benchmarking Theory of Mind in Large Language Models

ACL 2024long

Theory of Mind (ToM) is the cognitive capability to perceive and ascribe mental states to oneself and others. Recent research has sparked a debate over whether large language models (LLMs) exhibit a form of ToM. However, existing ToM evaluations are hindered by challenges such as constrained scope,…