Language Model

[TOC] Title: Scaling Expert Language Models With Unsupervised Domain Discovery Author: Luke Zettlemoyer et. al. Publish Year: 24 Mar, 2023 Review Date: Mon, Apr 3, 2023 url: https://arxiv.org/pdf/2303.14177.pdf Summary of paper Contribution we introduce a simple but efficient method to asynchronously train large, sparse language models on arbitrary text corpora. Our method clusters a corpus into sets of related documents, trains a separate expert language model on each cluster, and combines them in a sparse ensemble for inference. This approach generalise embarrassingly parallel training by automatically discovering the domain for each expert, and eliminates nearly all the communication overhead of existing sparse language models. Some key terms Cluster-Branch-Train-Merge (C-BTM) ...

[TOC] Title: How Robust Is GPT 3.5 to Predecessors a Comprehensive Study on Language Understanding Tasks Author: Xuanting Chen et. al. Publish Year: 2023 Review Date: Mon, Apr 3, 2023 url: https://arxiv.org/ftp/arxiv/papers/2303/2303.00293.pdf Summary of paper Motivation GPT3.5, their robustness, and abilities to handle various complexities of the open world have yet to be explored, which is especially crucial in assessing the stability of models and is a key aspect of trustworthy AI Contribution Our study yielded the following findings by comparing GPT 3.5 with finetuned models competitive results on test sets: GPT3.5 achieves SOTA results in some NLU tasks compared to supervised models fine-tuned with task-specific data. In particular GPT-3.5 performs well in reading comprehension and sentiment analysis tasks, but face challenges in sequence tagging and relation extraction tasks. Lack of robustness: GPT-3.5 still encounter significant robustness degradation, such as its average performance dropping by up to 35.74% and 43.59% in natural language inference and sentiment analysis tasks, respectively. However, it is worth noting that GPT3.5 achieves remarkable robustness on certain tasks, such as reading comprehension and WSC tasks Robustness instability: In few-shot scenarios, GPT-3.5’s robustness improvement varies greatly across different tasks. For example, GPT-3.5 shows significant improvement in aspect-based sentiment analysis tasks while the robustness actually decreases in natural language inference (Section 4.3.1) and semantic matching (Section 4.3.2) tasks. Prompt sensitivity: changes in input prompts have a significant impact on the results, and GPT-3.5’s robustness to prompt variations. still requires improvement. Number sensitivity: GPT3.5 is more sensitive to numerical inputs than pre-training fine-tuning models. For example, in the NumWord transformation, which involves replacing numerical words in sentences with different numerical values, GPT3.5 exhibits a significantly high level of sensitivity. Task labels sensitivity: we speculate that the task construction during the instruction tuning stage may significantly impact the model’s performance. In the case of IMDB binary sentiment classification dataset, the model outputs a large number of “neutral” responses, which are not included in the application label space, resulting in a performance drop Significant improvement in zero/few-shot scenarios: in zero-shot and few-shot scenario, GPT3.5 outperforms existing LLMs in most NLU tasks, especially in reading comprehension, natural language inference and semantic matching tasks Ability for in-context learning: Compared to 0-shot, GPT 3.5 performs better on most tasks in the 1-shot setting. Additionally, performance does no vary significantly between the 1-shot, 3-shot, 6-shot, 9-shot settings for most tasks. However, providing additional examples in the prompts can be advantageous for sequence tagging tasks

[TOC] Title: The Wisdom of Hindsight Makes Language Models Better Instruction Followers Author: Tianjun Zhang et. al. Publish Year: 10 Feb 2023 Review Date: Thu, Mar 2, 2023 url: https://arxiv.org/pdf/2302.05206.pdf Summary of paper Motivation Reinforcement learning with Human Feedback (RLHF) demonstrates impressive performance on the GPT series models. However, the pipeline for reward and value networks Contribution in this paper, we consider an alternative approach: converting feedback to instruction by relabeling the original one and training the model for better alignment in a supervised manner. Such an algorithm doesn’t require any additional parameters except for the original language model and maximally reuses the pretraining pipeline. To achieve this, we formulate instruction alignment problem in decision making. We propose Hindsight Instruction Relabeling (HIR), a novel algorithm for alignment language models with instructions. The resulting two-stage algorithm shed light to a family of reward-free approaches that utilise the hindsightly relabeled instructions based on feedback. Some key terms fine-tuning language model ...

[TOC] Title: Toolformer: Language Models Can Teach Themselves to Use Tools 2023 Author: Timo Schick et. al. META AI research Publish Year: 9 Feb 2023 Review Date: Wed, Mar 1, 2023 url: https://arxiv.org/pdf/2302.04761.pdf Summary of paper Motivation LMs exhibit remarkable abilities to solve new tasks from just a few examples or textual instructions, especially at scale. They also struggle with basic functionality, such as arithmetic or factual lookup. Contribution In this paper, we show that LMs can teach themselves to use external tools via simple APIs and achieve the best of both worlds. We introduce Toolformer, a model that incorporate a range of tools, including a calculator, a Q&A system, a search engine, a translation system and a calendar. Some key terms limitation of language models ...

[TOC] Title: Knowledge Is a Region in Weight Space for Fine Tuned Language Model Author: Almog Gueta et. al. Publish Year: 12 Feb 2023 Review Date: Wed, Mar 1, 2023 url: https://arxiv.org/pdf/2302.04863.pdf Summary of paper Motivation relatively little is known a bout the relationships between different models, especially those trained or tested on different datasets. Contribution we demonstrate that fine-tuned models that were optimized for high performance, reside in well-defined regions in weight space, and vice versa language models that have been fine-tuned on the same dataset form a tight cluster in the same weight space and that models fine-tuned on different datasets from the same underlying task form a looser cluster. traversing around the region between the models reaches new models that perform comparably or even better than models found via fine-tuning Our findings demonstrate that a model positioned between two similar models can acquire the knowledge of both. We leverage this finding and design a method to pick a better model for efficient fine-tuning. more findings ...