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Found 15 papers, 14 papers with code
Self-Explore to Avoid the Pit: Improving the Reasoning Capabilities of Language Models with Fine-grained Rewards
Training on large amounts of rationales (i. e., CoT Fine-tuning) is effective at improving the reasoning capabilities of large language models (LLMs).
INSTRUCTIR: A Benchmark for Instruction Following of Information Retrieval Models
Enhancing the capability of retrievers to understand intentions and preferences of users, akin to language model instructions, has the potential to yield more aligned search targets.
Carpe Diem: On the Evaluation of World Knowledge in Lifelong Language Models
In an ever-evolving world, the dynamic nature of knowledge presents challenges for language models that are trained on static data, leading to outdated encoded information.
FLASK: Fine-grained Language Model Evaluation based on Alignment Skill Sets
Evaluation of Large Language Models (LLMs) is challenging because instruction-following necessitates alignment with human values and the required set of skills varies depending on the instruction.
Improving Probability-based Prompt Selection Through Unified Evaluation and Analysis
Previous works in prompt engineering for large language models have introduced different gradient-free probability-based prompt selection methods that aim to choose the optimal prompt among the candidates for a given task but have failed to provide a comprehensive and fair comparison between each other.
The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning
Furthermore, we show that instruction tuning with CoT Collection allows LMs to possess stronger few-shot learning capabilities on 4 domain-specific tasks, resulting in an improvement of +2. 24% (Flan-T5 3B) and +2. 37% (Flan-T5 11B), even outperforming ChatGPT utilizing demonstrations until the max length by a +13. 98% margin.
Ranked #1 on
on BIG-bench (SNARKS)
Common Sense Reasoning
Common Sense Reasoning (Zero-Shot)
+7
Investigating the Effectiveness of Task-Agnostic Prefix Prompt for Instruction Following
In this paper, we present our finding that prepending a Task-Agnostic Prefix Prompt (TAPP) to the input improves the instruction-following ability of various Large Language Models (LLMs) during inference.
Exploring the Benefits of Training Expert Language Models over Instruction Tuning
Recently, Language Models (LMs) instruction-tuned on multiple tasks, also known as multitask-prompted fine-tuning (MT), have shown the capability to generalize to unseen tasks.
Ranked #9 on
Question Answering
on StoryCloze
Efficiently Enhancing Zero-Shot Performance of Instruction Following Model via Retrieval of Soft Prompt
Enhancing the zero-shot performance of instruction-following models requires heavy computation, either by scaling the total number of training datasets or the model size.
Guess the Instruction! Flipped Learning Makes Language Models Stronger Zero-Shot Learners
Meta-training, which fine-tunes the language model (LM) on various downstream tasks by maximizing the likelihood of the target label given the task instruction and input instance, has improved the zero-shot task generalization performance.
Ranked #2 on
Question Answering
on StoryCloze
Can Large Language Models Truly Understand Prompts? A Case Study with Negated Prompts
Previous work has shown that there exists a scaling law between the size of Language Models (LMs) and their zero-shot performance on different downstream NLP tasks.
TemporalWiki: A Lifelong Benchmark for Training and Evaluating Ever-Evolving Language Models
Language Models (LMs) become outdated as the world changes; they often fail to perform tasks requiring recent factual information which was absent or different during training, a phenomenon called temporal misalignment.
Towards Continual Knowledge Learning of Language Models
By highlighting the critical causes of knowledge forgetting, we show that CKL is a challenging and important problem that helps us better understand and train ever-changing LMs.
Efficient Contrastive Learning via Novel Data Augmentation and Curriculum Learning
We introduce EfficientCL, a memory-efficient continual pretraining method that applies contrastive learning with novel data augmentation and curriculum learning.
Dimensional Emotion Detection from Categorical Emotion
We present a model to predict fine-grained emotions along the continuous dimensions of valence, arousal, and dominance (VAD) with a corpus with categorical emotion annotations.
