| 08.03 |
Think-on-Graph: Deep and Responsible Reasoning of Large Language Model with Knowledge Graph |
Son, Suhyune |
link |
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| Chain of Knowledge: A Framework for Grounding Large Language Models with Structured Knowledge Bases |
| Rethinking with Retrieval: Faithful Large Language Model Inference |
| How Language Model Hallucinations Can Snowball |
Eo, Sugyeong |
link |
| From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Models |
| Detoxifying Text with MARCO: Controllable Revision with Experts and Anti-Experts |
| Generate rather than Retrieve: Large Language Models are Strong Context Generators |
Lee, Seungyoon |
link |
| Guess The Instruction! Flipped Learning Makes Language Models Strong Zero-Shot Learners |
| Leveraging Large Language Models For Multiple Choice Question Answering |
| 08.10 |
SELF-INSTRUCT: Aligning Language Models with Self-Generated Instructions |
Lee, Jeongwoo |
link |
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| WizardLM: Empowering Large Language Models to Follow Complex Instructions |
| Large Language Models Can Self-Improve |
| ZeRO: Memory Optimizations Toward Training Trillion Parameter Models |
Kim, Jeongwook |
link |
| ZeRO-Infinity: Breaking the GPU Memory Wall for Extreme Scale Deep Learning |
| Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism |
| Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning |
Moon, Hyeonseok |
link |
| PARAMETER-EFFICIENT FINE-TUNING DESIGN SPACES |
| Distill or Annotate? Cost-Efficient Fine-Tuning of Compact Models |
| 08.18 |
Linearly Mapping from Image to Text Space |
Lee, Jungseob |
link |
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| MAGMA – Multimodal Augmentation of Generative Models through Adapter-based Finetuning |
| MAPL: Parameter-Efficient Adaptation of Unimodal Pre-Trained Models for Vision-Language Few-Shot Prompting |
| Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models |
| Visual Instruction Tuning |
| LLaMA2: Open and Efficient Foundation Language Models |
Lee, Seungjun |
link |
| FLAN |
| G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment |
| Knowledge-Augmented Language Model Prompting for Zero-Shot Knowledge Graph Question Answering |
Lee, Jaewook |
link |
| Enhanced Story Comprehension for Large Language Models through Dynamic Document-Based Knowledge Graphs |
| ChatDB: Augmenting LLMs With Databases as Their Symbolic Memory |
| 08.24 |
LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention |
Hong, Seongtae |
link |
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| LLAMA-Adapter. V2: |
| LIMA: Less Is More for Alignment |
| Plug-and-Play Knowledge Injection for Pre-trained Language Models |
Jung, Dahyun |
link |
| Towards Continual Knowledge Learning of Language Models |
| Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback |
| HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Models |
Lim, Jungwoo |
link |
| Mitigating Language Model Hallucination with Interactive Question-Knowledge Alignment |
| PURR: Efficiently Editing Language Model Hallucinations by Denoising Language Model Corruptions |
| 08.31 |
fireball: a dataset of dungeons and dragons actual-play with structured game state information |
Kim, Jinsung |
link |
comments |
| marked personas: using natural language prompts to measure stereotypes in language models |
| What, When, and How to Ground: Designing User Persona-Aware Conversational Agents for Engaging Dialogue |
| Automatic Chain of Thought Prompting in Large Language Models |
Son, Junyoung |
link |
| Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models |
| Verify-and-Edit: A Knowledge-Enhanced Chain-of-Thought Framework |
| Zero-shot Faithful Factual Error Correction |
Kang, Myunghoon |
link |
| SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models |
| Language Models (Mostly) Know What They Know |
| 09.07 |
HellaSwag: Can a Machine Really Finish Your Sentence? |
Seo, Jaehyung |
link |
comments |
| Measuring Massive Multitask Language Understanding |
| Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge (ARC) |
| TruthfulQA: Measuring How Models Mimic Human Falsehoods |
| Clues Before Answers: Generation-Enhanced Multiple-Choice QA |
Koo, Seonmin |
link |
| Aligning Instruction Tasks Unlocks Large Language Models as Zero-Shot Relation Extractors |
| Say What You Mean! Large Language Models Speak Too Positively about Negative Commonsense Knowledge |
| LoRA: Low-Rank Adaptation of Large Language Models |
Jang, Yoonna |
link |
| Stack More Layers Differently: High-Rank Training Through Low-Rank Updates |
| LoraHub: Efficient Cross-Task Generalization via Dynamic LoRA Composition |
