Search Results for author:
Found 12 papers, 8 papers with code
Evaluating LLMs at Detecting Errors in LLM Responses
This work introduces ReaLMistake, the first error detection benchmark consisting of objective, realistic, and diverse errors made by LLMs.
TravelPlanner: A Benchmark for Real-World Planning with Language Agents
Are these language agents capable of planning in more complex settings that are out of the reach of prior AI agents?
Large Language Models for Mathematical Reasoning: Progresses and Challenges
Mathematical reasoning serves as a cornerstone for assessing the fundamental cognitive capabilities of human intelligence.
UMIE: Unified Multimodal Information Extraction with Instruction Tuning
Multimodal information extraction (MIE) gains significant attention as the popularity of multimedia content increases.
MUFFIN: Curating Multi-Faceted Instructions for Improving Instruction-Following
In the realm of large language models (LLMs), enhancing instruction-following capability often involves curating expansive training data.
Toward Zero-Shot Instruction Following
This work proposes a challenging yet more realistic setting for zero-shot cross-task generalization: zero-shot instruction following, presuming the existence of a paragraph-style task definition while no demonstrations exist.
Adaptive Chameleon or Stubborn Sloth: Revealing the Behavior of Large Language Models in Knowledge Conflicts
By providing external information to large language models (LLMs), tool augmentation (including retrieval augmentation) has emerged as a promising solution for addressing the limitations of LLMs' static parametric memory.
A Comprehensive Survey on Instruction Following
This survey paper tries to summarize and provide insights to the current research on instruction following, particularly, by answering the following questions: (i) What is task instruction, and what instruction types exist?
PAGE: A Position-Aware Graph-Based Model for Emotion Cause Entailment in Conversation
Conversational Causal Emotion Entailment (C2E2) is a task that aims at recognizing the causes corresponding to a target emotion in a conversation.
MORE: A Metric Learning Based Framework for Open-domain Relation Extraction
To address these problems, in this work, we propose a novel learning framework named MORE (Metric learning-based Open Relation Extraction).
GradTS: A Gradient-Based Automatic Auxiliary Task Selection Method Based on Transformer Networks
Compared to AUTOSEM, a strong baseline method, GradTS improves the performance of MT-DNN with a bert-base-cased backend model, from 0. 33% to 17. 93% on 8 natural language understanding (NLU) tasks in the GLUE benchmarks.
Contributions of Transformer Attention Heads in Multi- and Cross-lingual Tasks
Through extensive experiments, we show that (1) pruning a number of attention heads in a multi-lingual Transformer-based model has, in general, positive effects on its performance in cross-lingual and multi-lingual tasks and (2) the attention heads to be pruned can be ranked using gradients and identified with a few trial experiments.
