Large Language Models aren't all that you need

• URL: http://arxiv.org/abs/2401.00698v1
• Date: Mon, 1 Jan 2024 08:32:50 GMT
• Title: Large Language Models aren't all that you need
• Authors: Kiran Voderhobli Holla, Chaithanya Kumar, Aryan Singh
• Abstract summary: This paper describes the architecture and systems built towards solving the SemEval 2023 Task 2: MultiCoNER II. We evaluate two approaches (a) a traditional Random Fields model and (b) a Large Language Model (LLM) fine-tuned with a customized head and compare the two approaches.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper describes the architecture and systems built towards solving the SemEval 2023 Task 2: MultiCoNER II (Multilingual Complex Named Entity Recognition) [1]. We evaluate two approaches (a) a traditional Conditional Random Fields model and (b) a Large Language Model (LLM) fine-tuned with a customized head and compare the two approaches. The novel ideas explored are: 1) Decaying auxiliary loss (with residual) - where we train the model on an auxiliary task of Coarse-Grained NER and include this task as a part of the loss function 2) Triplet token blending - where we explore ways of blending the embeddings of neighboring tokens in the final NER layer prior to prediction 3) Task-optimal heads - where we explore a variety of custom heads and learning rates for the final layer of the LLM. We also explore multiple LLMs including GPT-3 and experiment with a variety of dropout and other hyperparameter settings before arriving at our final model which achieves micro & macro f1 of 0.85/0.84 (on dev) and 0.67/0.61 on the test data . We show that while pre-trained LLMs, by themselves, bring about a large improvement in scores as compared to traditional models, we also demonstrate that tangible improvements to the Macro-F1 score can be made by augmenting the LLM with additional feature/loss/model engineering techniques described above.

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