Papers with BC

6 papers
Probing the Multi-turn Planning Capabilities of LLMs via 20 Question Games (2024.acl-long)

Copied to clipboard

Challenge: Large language models (LLMs) are effective at answering clear questions but when faced with ambiguous queries they act unpredictably and produce incorrect outputs.
Approach: They propose to use a surrogate problem to assess an LLMs’s ability to deduce an entity unknown to itself, but revealed to a judge, by asking the judge a series of queries.
Outcome: The proposed model outperforms human players on the entity-deducing task by a large margin.
BPM_MT: Enhanced Backchannel Prediction Model using Multi-Task Learning (2021.emnlp-main)

Copied to clipboard

Challenge: Backchannel (BC) is a short and quick reaction signal of a listener to a speaker's utterances.
Approach: They propose a model that utilizes lexical information in utterances to enhance backchannel (BC) prediction.
Outcome: The proposed model showed 14.24% performance improvement compared to baseline in the four BC categories: continuer, understanding, empathic response, and No BC.
Eliciting Motivational Interviewing Skill Codes in Psychotherapy with LLMs: A Bilingual Dataset and Analytical Study (2024.lrec-main)

Copied to clipboard

Challenge: Motivational interviewing (MI) is an essential, directive, client-centered counseling technique.
Approach: They propose a bilingual dataset of MI conversations in English and Dutch . they propose an approach to elicit MISC expertise from Large language models .
Outcome: The proposed approach yields results aligned with expert annotations and maintains consistent performance across languages.
Ancient Chinese Glyph Identification Powered by Radical Semantics (2024.findings-acl)

Copied to clipboard

Challenge: Currently, about half of ancient Chinese glyphs have not been deciphered yet.
Approach: They propose to use a Chinese glyph knowledge graph to infer the Chinese character label for the unknown ancient Chinese . they propose to combine the visual, textual, and the graph data to create a multimodal Chinese morph identification framework.
Outcome: The proposed method can identify ancient Chinese characters from 1300 BC to 200 BC based on image and radical semantics on a 1000-year-old Chinese glyph dataset.
Minimal Yet Big Impact: How AI Agent Back-channeling Enhances Conversational Engagement through Conversation Persistence and Context Richness (2024.findings-emnlp)

Copied to clipboard

Challenge: Increasing use of AI agents in conversational services highlights the importance of back-channeling (BC) as an active listening strategy to enhance conversational engagement.
Approach: They conducted an experiment with 55 participants to evaluate conversational engagement using both quantitative and qualitative metrics.
Outcome: The results show that the Todak_BC and TodAK_NoBC groups have significantly higher conversational engagement than the Todask_NoB.
MM-JudgeBias: A Benchmark for Evaluating Compositional Biases in MLLM-as-a-Judge (2026.acl-long)

Copied to clipboard

Challenge: Multimodal Large Language Models (MLLMs) are increasingly used as automatic judges . however, their reliability and vulnerabilities to biases remain underexplored .
Approach: They propose a benchmark to evaluate MLLMs that fail to integrate visual cues . they also introduce a test to evaluate the reliability of MLMLs based on a set of asymmetric evaluation tendencies.
Outcome: Experiments on 26 state-of-the-art MLLMs reveal modality neglect and asymmetric evaluation tendencies . a standardized model with a benchmark enables a fine-grained diagnosis of nine bias types .

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations