Large Language Models (LLMs) are evolving rapidly on code generation tasks. While it is important to evaluate their code generation accuracy, ensuring they follow responsible practices is equally critical. Some of the previous works use tools such as CodeQL to match patterns against Common Weakness Enumeration (CWE), suffering from high error rate, while others rely on human annotation to only focus on top CWE categories, limiting security coverage. We propose AutoSUIT Bench, which addresses these limitations through a paradigm to automate the vulnerable code benchmark creation with iterative auto validation. As a result, our benchmark covers 232 CWE categories across C/C++, Java, and Python languages and is designed to evaluate on four coding tasks: (i) code generation, (ii) generation with CWE context, (iii) security patching, and (iv) code completion. Upon benchmarking against LLMs, we found that functionality pass rate is consistently higher than vulnerability pass rate for all programming languages. One notable observation from our benchmark is that LLMs perform well on top CWEs while lacks on others down the list. This highlights the necessity of vulnerable code benchmarks with larger CWE coverage.

The context of modern smart voice assistants is often multi-modal, where images, audio and video content are consumed by users simultaneously. In such a setup, co-reference resolution is especially challenging, and runs across modalities and dialogue turns. We explore the problem of multi-modal co-reference resolution in multi-turn dialogues and quantify the performance of multi-modal LLMs on a specially curated dataset of long, image-interleaved conversations between a voice assistant and human in a shopping use case. We propose a custom architecture for multi-modal embedding alignment using a novel parameter augmentation technique. Our proposed Parameter Augmented LLM approach shows a 4.9% absolute F1 improvement above a cross-attention baseline while reducing the number of parameters being trained by 4x.

This paper presents UMASS_BioNLP team participation in the MEDIQA-Chat 2023 shared task for Task-A and Task-C. We focus especially on Task-C and propose a novel LLMs cooperation system named a doctor-patient loop to generate high-quality conversation data sets. The experiment results demonstrate that our approaches yield reasonable performance as evaluated by automatic metrics such as ROUGE, medical concept recall, BLEU, and Self-BLEU. Furthermore, we conducted a comparative analysis between our proposed method and ChatGPT and GPT-4. This analysis also investigates the potential of utilizing cooperation LLMs to generate high-quality datasets.