3 citations · 25 across the 55 of their papers we have counts for
6 papers · 2 filters
Balancing Memory Pathways: Analyzing and Improving Memory Utilization in Hybrid LMs
Hyunji Lee, Joykirat Singh, Zaid Khan +5
Recurrent-attention hybrid language models (LMs), which interleave attention and recurrent layers, are increasingly used to combine the efficiency of the recurrent layers with the…
CreativeInstruct: Scalably Teaching LLMs to Balance Quality, Creativity, and Diversity
Ananya Sahu, Mohit Bansal, Elias Stengel-Eskin
While post-training improves the capabilities of large language models (LLMs), it generally lowers their output diversity and creativity, negatively impacting tasks that explicitly…
MINTEval: Evaluating Memory under Multi-Target Interference in Long-Horizon Agent Systems
Hyunji Lee, Justin Chih-Yao Chen, Joykirat Singh +3
Real-world agents operate over long and evolving horizons, where information is repeatedly updated and may interfere across memories, requiring accurate recall and aggregated reaso…
Playing Along: Learning a Double-Agent Defender for Belief Steering via Theory of Mind
Hanqi Xiao, Vaidehi Patil, Zaid Khan +3
As large language models (LLMs) become the engine behind conversational systems, their ability to reason about the intentions and states of their dialogue partners (i.e., form and…
Balancing Faithfulness and Performance in Reasoning via Multi-Listener Soft Execution
Nithin Sivakumaran, Shoubin Yu, Hyunji Lee +4
Chain-of-thought (CoT) reasoning sometimes fails to faithfully reflect the true computation of a large language model (LLM), hampering its utility in explaining how LLMs arrive at…
Routing with Generated Data: Annotation-Free LLM Skill Estimation and Expert Selection
Tianyi Niu, Justin Chih-Yao Chen, Genta Indra Winata +6
Large Language Model (LLM) routers dynamically select optimal models for given inputs. Existing approaches typically assume access to ground-truth labeled data, which is often unav…