5 research outputs found
SAIE Framework: Support Alone Isn't Enough -- Advancing LLM Training with Adversarial Remarks
Large Language Models (LLMs) can justify or criticize their predictions
through discussion with other models or humans, thereby enhancing their
intrinsic understanding of instances. While proactive discussions enhance
performance, this approach is currently limited to the inference phase. In this
context, we posit a hypothesis: learning interactive discussions during
training can improve understanding for the instances in the training step and
proficiency in logical/critical thinking ability and verbalized expression of
the model in the inference step. Our proposed SAIE training method involves
both supportive and adversarial discussions between the learner and partner
models. The learner model receives a remark from the partner through the
discussion, and the parameters of the learner model are then updated based on
this remark. That is, the teacher signal dynamically adjusts in response to the
evolving model output throughout the training step. By bolstering the capacity
for discussion and comprehension of instances, our experiments across datasets,
including GSM8K, CommonsenseQA, and MMLU, reveal that models fine-tuned with
our method consistently surpass those trained with standard fine-tuning
techniques. Moreover, our approach demonstrates superior performance in
multi-agent inference scenarios, boosting the models' reasoning abilities at
the inference step.Comment: Work in progres
Exploring Effectiveness of GPT-3 in Grammatical Error Correction: A Study on Performance and Controllability in Prompt-Based Methods
Large-scale pre-trained language models such as GPT-3 have shown remarkable
performance across various natural language processing tasks. However, applying
prompt-based methods with GPT-3 for Grammatical Error Correction (GEC) tasks
and their controllability remains underexplored. Controllability in GEC is
crucial for real-world applications, particularly in educational settings,
where the ability to tailor feedback according to learner levels and specific
error types can significantly enhance the learning process. This paper
investigates the performance and controllability of prompt-based methods with
GPT-3 for GEC tasks using zero-shot and few-shot setting. We explore the impact
of task instructions and examples on GPT-3's output, focusing on controlling
aspects such as minimal edits, fluency edits, and learner levels. Our findings
demonstrate that GPT-3 could effectively perform GEC tasks, outperforming
existing supervised and unsupervised approaches. We also showed that GPT-3
could achieve controllability when appropriate task instructions and examples
are given.Comment: Accepted in BEA 202