57 citations · 70 across the 16 of their papers we have counts for
16 papers
Selective Regenerative Decoding: Trajectory-Level Intervention for Inference-Time Reasoning
Sophia Xiao Pu, Yumo Xu, Sailik Sengupta +5
Inference-time decoding methods improve LLM reasoning by exploring multiple candidate trajectories, yet treat each trajectory as atomic: either retaining it whole or discarding it…
MemToolAgent: Leveraging Memory for Tool Using Agents Based on Environment and User Feedback
Suleyman Armagan Er, Danilo Ribeiro, Yogesh Virkar +5
Modern large language model (LLM) agents can use external tools to help users solve complex tasks. However, for problems that require learning from long-term historical events or f…
ConvApparel: A Benchmark Dataset and Validation Framework for User Simulators in Conversational Recommenders
Ofer Meshi, Krisztian Balog, Sally Goldman +5
The promise of LLM-based user simulators to improve conversational AI is hindered by a critical "realism gap," leading to systems that are optimized for simulated interactions, but…
CiteEval: Principle-Driven Citation Evaluation for Source Attribution
Yumo Xu, Peng Qi, Jifan Chen +7
Citation quality is crucial in information-seeking systems, directly influencing trust and the effectiveness of information access. Current evaluation frameworks, both human and au…
InsTALL: Context-aware Instructional Task Assistance with Multi-modal Large Language Models
Pha Nguyen, Sailik Sengupta, Girik Malik +2
The improved competence of generative models can help building multi-modal virtual assistants that leverage modalities beyond language. By observing humans performing multi-step ta…
FLAP: Flow-Adhering Planning with Constrained Decoding in LLMs
Shamik Roy, Sailik Sengupta, Daniele Bonadiman +2
Planning is a crucial task for agents in task oriented dialogs (TODs). Human agents typically resolve user issues by following predefined workflows, decomposing workflow steps into…