34 citations · 44 across the 12 of their papers we have counts for
5 papers · 1 filter
Extending LLMs' Context Window with 100 Samples
Yikai Zhang, Junlong Li, Pengfei Liu
Large Language Models (LLMs) are known to have limited extrapolation ability beyond their pre-trained context window, constraining their application in downstream tasks with length…
InFoBench: Evaluating Instruction Following Ability in Large Language Models
Yiwei Qin, Kaiqiang Song, Yebowen Hu +7
This paper introduces the Decomposed Requirements Following Ratio (DRFR), a new metric for evaluating Large Language Models' (LLMs) ability to follow instructions. Addressing a gap…
Let's reward step by step: Step-Level reward model as the Navigators for Reasoning
Qianli Ma, Haotian Zhou, Tingkai Liu +4
Recent years have seen considerable advancements in multi-step reasoning with Large Language Models (LLMs). The previous studies have elucidated the merits of integrating feedback…
Deep Multi-Task Learning with Shared Memory
Pengfei Liu, Xipeng Qiu, Xuanjing Huang
Neural network based models have achieved impressive results on various specific tasks. However, in previous works, most models are learned separately based on single-task supervis…
Syntax-based Attention Model for Natural Language Inference
PengFei Liu, Xipeng Qiu, Xuanjing Huang
Introducing attentional mechanism in neural network is a powerful concept, and has achieved impressive results in many natural language processing tasks. However, most of the exist…