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Found 17 papers, 6 papers with code
Strength Lies in Differences! Towards Effective Non-collaborative Dialogues via Tailored Strategy Planning
We investigate non-collaborative dialogue agents that must engage in tailored strategic planning for diverse users to secure a favorable agreement.
AMOR: A Recipe for Building Adaptable Modular Knowledge Agents Through Process Feedback
We present AMOR, an agent framework based on open-source LLMs, which reasons with external knowledge bases and adapts to specific domains through human supervision to the reasoning process.
Multi-granularity Correspondence Learning from Long-term Noisy Videos
Existing video-language studies mainly focus on learning short video clips, leaving long-term temporal dependencies rarely explored due to over-high computational cost of modeling long videos.
Ranked #1 on
Zero-Shot Video Retrieval
on YouCook2
Action Segmentation
Long Video Retrieval (Background Removed)
+2
Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems
Based on this, we propose a comprehensive taxonomy, which systematically analyzes potential risks associated with each module of an LLM system and discusses the corresponding mitigation strategies.
Multi-view Hypergraph Contrastive Policy Learning for Conversational Recommendation
Specifically, MHCPL timely chooses useful social information according to the interactive history and builds a dynamic hypergraph with three types of multiplex relations from different views.
Knowing-how & Knowing-that: A New Task for Machine Comprehension of User Manuals
The machine reading comprehension (MRC) of user manuals has huge potential in customer service.
Towards Hierarchical Policy Learning for Conversational Recommendation with Hypergraph-based Reinforcement Learning
Conversational recommendation systems (CRS) aim to timely and proactively acquire user dynamic preferred attributes through conversations for item recommendation.
Robust Domain Adaptation for Machine Reading Comprehension
Such a process will inevitably introduce mismatched pairs (i. e., noisy correspondence) due to i) the unavailable QA pairs in target documents, and ii) the domain shift during applying the QA construction model to the target domain.
AdaCoach: A Virtual Coach for Training Customer Service Agents
With the development of online business, customer service agents gradually play a crucial role as an interface between the companies and their customers.
Utterance Rewriting with Contrastive Learning in Multi-turn Dialogue
Context modeling plays a significant role in building multi-turn dialogue systems.
Towards Generalized Models for Task-oriented Dialogue Modeling on Spoken Conversations
Building robust and general dialogue models for spoken conversations is challenging due to the gap in distributions of spoken and written data.
A Dialogue-based Information Extraction System for Medical Insurance Assessment
In the Chinese medical insurance industry, the assessor's role is essential and requires significant efforts to converse with the claimant.
R2D2: Recursive Transformer based on Differentiable Tree for Interpretable Hierarchical Language Modeling
Human language understanding operates at multiple levels of granularity (e. g., words, phrases, and sentences) with increasing levels of abstraction that can be hierarchically combined.
SeaD: End-to-end Text-to-SQL Generation with Schema-aware Denoising
In text-to-SQL task, seq-to-seq models often lead to sub-optimal performance due to limitations in their architecture.
Interactive Question Clarification in Dialogue via Reinforcement Learning
In this work, we propose a reinforcement model to clarify ambiguous questions by suggesting refinements of the original query.
Query Distillation: BERT-based Distillation for Ensemble Ranking
We present a novel two-stage distillation method for ranking problems that allows a smaller student model to be trained while benefitting from the better performance of the teacher model, providing better control of the inference latency and computational burden.
Long Short-Term Sample Distillation
The long-term teacher draws on snapshots from several epochs ago in order to provide steadfast guidance and to guarantee teacher--student differences, while the short-term one yields more up-to-date cues with the goal of enabling higher-quality updates.
