244 citations · 245 across the 11 of their papers we have counts for
11 papers
AI Research Preference Models
Thomas Simon Foster, Bassel Al Omari, Tingchen Fu +30
AI research agents (AIRA) can now carry machine learning experiments from proposal through implementation and evaluation. Yet progress on frontier tasks is throttled by the cost of…
Asking the Right Questions: Improving Reasoning with Generated Stepping Stones
Hengyuan Hu, Tingchen Fu, Minqi Jiang +3
Recent years have witnessed tremendous progress in enabling LLMs to solve complex reasoning tasks such as math and coding. As we start to apply LLMs to harder tasks that they may n…
Unlocking Decoding-time Controllability: Gradient-Free Multi-Objective Alignment with Contrastive Prompts
Tingchen Fu, Yupeng Hou, Julian McAuley +1
The task of multi-objective alignment aims at balancing and controlling the different alignment objectives (e.g., helpfulness, harmlessness and honesty) of large language models to…
On the Transformations across Reward Model, Parameter Update, and In-Context Prompt
Deng Cai, Huayang Li, Tingchen Fu +11
Despite the general capabilities of pre-trained large language models (LLMs), they still need further adaptation to better serve practical applications. In this paper, we demonstra…
Disperse-Then-Merge: Pushing the Limits of Instruction Tuning via Alignment Tax Reduction
Tingchen Fu, Deng Cai, Lemao Liu +2
Supervised fine-tuning (SFT) on instruction-following corpus is a crucial approach toward the alignment of large language models (LLMs). However, the performance of LLMs on standar…
BBA: Bi-Modal Behavioral Alignment for Reasoning with Large Vision-Language Models
Xueliang Zhao, Xinting Huang, Tingchen Fu +5
Multimodal reasoning stands as a pivotal capability for large vision-language models (LVLMs). The integration with Domain-Specific Languages (DSL), offering precise visual represen…
Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models
Yue Zhang, Yafu Li, Leyang Cui +13
While large language models (LLMs) have demonstrated remarkable capabilities across a range of downstream tasks, a significant concern revolves around their propensity to exhibit h…
Towards Efficient Dialogue Pre-training with Transferable and Interpretable Latent Structure
Xueliang Zhao, Lemao Liu, Tingchen Fu +3
With the availability of massive general-domain dialogue data, pre-trained dialogue generation appears to be super appealing to transfer knowledge from the general domain to downst…
There Is No Standard Answer: Knowledge-Grounded Dialogue Generation with Adversarial Activated Multi-Reference Learning
Xueliang Zhao, Tingchen Fu, Chongyang Tao +1
Knowledge-grounded conversation (KGC) shows excellent potential to deliver an engaging and informative response. However, existing approaches emphasize selecting one golden knowled…
Learning to Express in Knowledge-Grounded Conversation
Xueliang Zhao, Tingchen Fu, Chongyang Tao +3
Grounding dialogue generation by extra knowledge has shown great potentials towards building a system capable of replying with knowledgeable and engaging responses. Existing studie…
There Are a Thousand Hamlets in a Thousand People's Eyes: Enhancing Knowledge-grounded Dialogue with Personal Memory
Tingchen Fu, Xueliang Zhao, Chongyang Tao +2
Knowledge-grounded conversation (KGC) shows great potential in building an engaging and knowledgeable chatbot, and knowledge selection is a key ingredient in it. However, previous…