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Instruction Following

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보존된 평가 기록: 4

Instruction following is the basic task of the model. This task is dedicated to evaluating the ability of the large model to follow human instructions. It is hoped that the model can generate controllable and safe answers.

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/riflezhang/genco

공식 코드

Generation-driven Contrastive Self-training for Zero-shot Text Classification with Instruction-following LLM

https://github.com/riflezhang/llava-hound-dpo

공식 코드

Direct Preference Optimization of Video Large Multimodal Models from Language Model Reward

https://github.com/rucaibox/quantizedempirical

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Do Emergent Abilities Exist in Quantized Large Language Models: An Empirical Study

https://github.com/rui-ye/openfedllm

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OpenFedLLM: Training Large Language Models on Decentralized Private Data via Federated Learning

https://github.com/rupertluo/valley

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Valley: Video Assistant with Large Language model Enhanced abilitY

https://github.com/ryuryukke/HowYouPromptMatters

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How You Prompt Matters! Even Task-Oriented Constraints in Instructions Affect LLM-Generated Text Detection

https://github.com/s1s-z/gateau

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GATEAU: Selecting Influential Samples for Long Context Alignment

https://github.com/saehyung-lee/plugir

공식 코드

Interactive Text-to-Image Retrieval with Large Language Models: A Plug-and-Play Approach

https://github.com/safeailab/eagle

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EAGLE: Speculative Sampling Requires Rethinking Feature Uncertainty

https://github.com/sail-sg/sdft

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Self-Distillation Bridges Distribution Gap in Language Model Fine-Tuning

https://github.com/salesforce/lavis

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BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models

https://github.com/salesforceairesearch/contextualjudgebench

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Does Context Matter? ContextualJudgeBench for Evaluating LLM-based Judges in Contextual Settings

https://github.com/salesforceairesearch/findap

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Demystifying Domain-adaptive Post-training for Financial LLMs

https://github.com/salesforceairesearch/jetts-benchmark

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Evaluating Judges as Evaluators: The JETTS Benchmark of LLM-as-Judges as Test-Time Scaling Evaluators

https://github.com/salt-nlp/chain-of-thought-bias

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On Second Thought, Let's Not Think Step by Step! Bias and Toxicity in Zero-Shot Reasoning

https://github.com/salt-nlp/unintended-impacts-of-alignment

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Unintended Impacts of LLM Alignment on Global Representation

https://github.com/satsuki2486441738/fusionaudio

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FusionAudio-1.2M: Towards Fine-grained Audio Captioning with Multimodal Contextual Fusion

https://github.com/savadikarc/gift

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Generative Parameter-Efficient Fine-Tuning

https://github.com/scai-jhu/realwebassist

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RealWebAssist: A Benchmark for Long-Horizon Web Assistance with Real-World Users

https://github.com/scalingintelligence/archon

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Archon: An Architecture Search Framework for Inference-Time Techniques

https://github.com/seonghyeonye/icil

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Investigating the Effectiveness of Task-Agnostic Prefix Prompt for Instruction Following

https://github.com/seonghyeonye/rospr

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Efficiently Enhancing Zero-Shot Performance of Instruction Following Model via Retrieval of Soft Prompt

https://github.com/seonghyeonye/tapp

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Investigating the Effectiveness of Task-Agnostic Prefix Prompt for Instruction Following

https://github.com/sheldonwu0327/lif-bench-2024

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LIFBench: Evaluating the Instruction Following Performance and Stability of Large Language Models in Long-Context Scenarios

https://github.com/shenao-zhang/reward-augmented-preference

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Reward-Augmented Data Enhances Direct Preference Alignment of LLMs

https://github.com/shenao-zhang/selm

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Self-Exploring Language Models: Active Preference Elicitation for Online Alignment

https://github.com/shi-labs/cumo

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CuMo: Scaling Multimodal LLM with Co-Upcycled Mixture-of-Experts

https://github.com/shin-ee-chen/SIFo

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The SIFo Benchmark: Investigating the Sequential Instruction Following Ability of Large Language Models

https://github.com/showlab/showui

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ShowUI: One Vision-Language-Action Model for GUI Visual Agent

https://github.com/shtuplus/iccc_cvpr2024

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Learning by Correction: Efficient Tuning Task for Zero-Shot Generative Vision-Language Reasoning

https://github.com/simon-wan/fiser

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Infer Human's Intentions Before Following Natural Language Instructions

https://github.com/sino-huang/brittleness_of_lrs

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A Reminder of its Brittleness: Language Reward Shaping May Hinder Learning for Instruction Following Agents

https://github.com/skddj/loldu

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LoLDU: Low-Rank Adaptation via Lower-Diag-Upper Decomposition for Parameter-Efficient Fine-Tuning

https://github.com/sled-group/danli

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DANLI: Deliberative Agent for Following Natural Language Instructions

https://github.com/slit-ai/fusechat-3.0

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FuseChat-3.0: Preference Optimization Meets Heterogeneous Model Fusion

https://github.com/sneheshs/natsgld

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NatSGLD: A Dataset with Speech, Gesture, Logic, and Demonstration for Robot Learning in Natural Human-Robot Interaction

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