1 citations · 2 across the 17 of their papers we have counts for
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Learning Latent Reasoning Traces for Scalar Reward Models End-to-End
Sanwoo Lee, Clive Bai, Hsiu-Yuan Huang +3
Reward models (RMs) are central to aligning large language models with human preferences via reinforcement learning. Although traditional scalar RMs enable efficient and probabilis…
ActTraitBench: Quantifying the Knowledge-Decision Gap in Large Language Models via Human-Grounded Behavioral Validation
Yutong Yang, Chenxi Miao, Weikang Li +1
While Large Language Models (LLMs) can convincingly simulate personas in explicit self-reports, they often deviate in implicit behavioral decisions, revealing a substantial Knowled…
CFMS: Towards Explainable and Fine-Grained Chinese Multimodal Sarcasm Detection Benchmark
Junzhao Zhang, Hsiu-Yuan Huang, Chenming Tang +2
Multimodal sarcasm detection has recently garnered significant attention. However, existing benchmarks suffer from coarse-grained annotations and limited cultural coverage, which h…
Aligning Language Models with Real-time Knowledge Editing
Chenming Tang, Yutong Yang, Kexue Wang +1
Knowledge editing aims to modify outdated knowledge in language models efficiently while retaining their original capabilities. Mainstream datasets for knowledge editing are predom…
Beyond Demonstrations: Dynamic Vector Construction from Latent Representations
Wang Cai, Hsiu-Yuan Huang, Zhixiang Wang +1
In-Context derived Vector (ICV) methods extract task-relevant representations from large language models (LLMs) and reinject them during inference, achieving comparable performance…
Lost in the Passage: Passage-level In-context Learning Does Not Necessarily Need a "Passage"
Hao Sun, Chenming Tang, Gengyang Li +1
By simply incorporating demonstrations into the context, in-context learning (ICL) enables large language models (LLMs) to yield awesome performance on many tasks. In this study, w…
SCOI: Syntax-augmented Coverage-based In-context Example Selection for Machine Translation
Chenming Tang, Zhixiang Wang, Yunfang Wu
In-context learning (ICL) greatly improves the performance of large language models (LLMs) on various down-stream tasks, where the improvement highly depends on the quality of demo…
Large Language Models Might Not Care What You Are Saying: Prompt Format Beats Descriptions
Chenming Tang, Zhixiang Wang, Hao Sun +1
With the help of in-context learning (ICL), large language models (LLMs) have achieved impressive performance across various tasks. However, the function of descriptive instruction…
Ungrammatical-syntax-based In-context Example Selection for Grammatical Error Correction
Chenming Tang, Fanyi Qu, Yunfang Wu
In the era of large language models (LLMs), in-context learning (ICL) stands out as an effective prompting strategy that explores LLMs' potency across various tasks. However, apply…
Going Beyond Word Matching: Syntax Improves In-context Example Selection for Machine Translation
Chenming Tang, Zhixiang Wang, Yunfang Wu
In-context learning (ICL) is the trending prompting strategy in the era of large language models (LLMs), where a few examples are demonstrated to evoke LLMs' power for a given task…