Weekly Updates for Saif Uddin Mahmud

Saif:

Hello group,

Milestone: Diagnostic Evaluation (Tentative - First Week of April)

Current work: Investigating Sparse AutoEncoders for LLM Explanation

I have been studying about the approaches of LLM explanation. I came along this paper that discusses the existing techniques and challenges of the approaches: “Explainability for Large Language Models: A Survey” - by H Zhao · 2024

From the existing approaches, I would like to explore the following two–

Feature Interpretability via Concept Vectors (CAVs):

What it is: Use post-hoc linear classifiers or other mechanisms to define high-level concept vectors in the latent space that align with human-understandable constructs (e.g., sentiment, gender).

Applications:

Explain model decisions in human terms by projecting latent representations onto concept vectors.

Diagnose the presence of biases or specific abstract features (e.g., fairness in language models).

Sparse Feature Extraction:

What it is: Neuroscience-inspired methods like sparse autoencoders or dictionary learning extract sparse or disentangled features from LLM activation spaces.

Replies:

Dr. Lei:

as i suggested, focus on the big picture first before you dive into the details of a particular approach

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Saif:

Hello group,

Milestone: Diagnostic Evaluation (Tentative - First Week of April)

Current work: Investigating Sparse AutoEncoders for LLM Explanation

Action items done in the previous week:

– Regarding fundamentals of SAEs, I came along with the following:

1. Ng, Andrew. “Sparse autoencoder.” CS294A Lecture notes 72.2011 (2011): 1-19.

1. Makhzani, Alireza, and Brendan Frey. “K-sparse autoencoders.” arXiv preprint arXiv:1312.5663 (2013).

Action items for next week:

– My primary plan for this week is to look for small open source SAE models that can be trained in smaller scale

– And continue grouping the papers

Replies:

Dr. Lei:

i suggest you think more about the problem, i.e., explaining LLM models. in addition to SAEs, what are other approaches to LLM explanations? what are the general technical problems, and what the existing approaches to solving these problems?

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Saif:

Ok Professor. I will look into this according to your suggestion.

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Saif:

Hello group,

Milestone: Diagnostic Evaluation(Tentative - First Week of April)

Current work: Investigating Sparse AutoEncoders for LLM Explanation

Action items completed:

• After the Friday meeting, I have compiled a list of 50 papers on Sparse Autoencoders (SAE) for LLM explanation. I have added the comprehensive list to my channel.

• After skimming through the papers, I have come up with the key findings (references I am using here can be found in the shared document):

1. Utility of SAEs for LLM Interpretability:

• SAEs successfully extract interpretable, sparse, and often monosemantic features from dense LLM activations, addressing the superposition problem where multiple features are combined in single neurons [1, 2, 48].

• Applications include identifying causal dependencies, debugging model behaviors, improving model safety (e.g., hallucinations, refusals), and analyzing circuits like attention heads [3, 6].

2. Challenges in Implementation:

• Scalability: SAEs must manage the high dimensionality of LLM activations (e.g., residual streams or attention) while maintaining computational efficiency. Techniques like layer-wise transfer learning [29] and grouping layers [21] have been proposed to address this.

• Reconstruction Fidelity vs. Sparsity: Achieving a balance between sparsity and reconstruction fidelity remains difficult, especially when minimizing nonlinear reconstruction errors [7, 9].

Team meeting update:

I was working on compiling the papers. There was not much to discuss this week so we skipped the meeting.

Action items for this week:

I will continue summarizing the papers from the complied list. I would like to sign up for a technical discussion on Friday on my finding on this topic.

Replies:

Dr. Lei:

looks good. keep up with the good work

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Saif:

Good morning group,

Milestone: Diagnostic Evaluation (Tentative - First Week of April)

• This is my 3rd semester running and most of my required course work is complete. I would like to sit for diagnostic evaluation. I may need some suggestions from the group regarding the process.

Milestone: Literature Review Draft - January 23, 2025

• During the break, I was unable to commit any time on research due to some unavoidable circumstances. I would like to push the deadline two more weeks.

Collaboration with Fadul:

• We had a meeting on Saturday regarding potential research directions for collaboration. We have identified two topics that we may opt for — a) Sparse Auto Encoders for explaining LLMs, and b) Tabular Data Generation. The meeting minutes have been posted on our team’s channel. My plan for this week to go over the papers suggested by Fadul to gain background knowledge on these two topics.

Replies:

Dr. Lei:

sounds good. please work with Fadul and Sunny on possible new topics. the current focus for you is to find a topic to dive into in the next two weeks.

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No update was provided for the week ending 2024-12-18.

Saif:

Hi Group,

Updated Milestone:Literature Review Draft - January 23, 2025

Based on the feedback on Friday’s meeting, I have updated the timeline for the literature review.

Action Items from last week:

After Friday, I refined the study design section, added research questions, and identified that performance metric summaries needed to be included..

• I have edited the study design section that includes the details of article search process, keyword selection for query terms, and exclusion criteria.

• I have added research questions based on machine learning approaches, dataset, feature selection, preprocessing mechanisms and performance metrics.

• When I was working on defining the research questions, I noticed that I have missed summarizing the performance metrics discussed in the papers. This is a major part of the review. I am going over the papers to note down the performance metrics.

I have an exam to proctor today and after that the Professor would like to discuss the grading with me. I might be late to join the meeting.

Replies:

Dr. Lei:

this is probably your meaningful status update, which i am really happen to see. keep it up

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Saif:

Thank you Dr. Lei.

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Saif:

Action items Completed:

• I have Completed summaries for all the papers and completed the grouping based on the summaries

• I have created a skeleton for the review paper and started writing the draft.

We had a general discussion with Fadul regarding collaboration.

Saif:

Hi group,

I had Compilers final exam on Friday and two project submissions until yesterday. I couldn’t spend much time on research.

I will be presenting the new idea today.

Saif:

Milestone: Complete Literature Survey - Dec 5

• Action items from last week–

• I have outlined 5 more papers for the literature survey [I was working on final projects on compiler and distributed systems. was unable to spend more time on reading papers]

• Action Items for next week:

• New Idea presentation

• End of November 23: Write a clear outline for the review on shared overleaf project. each section linked to relevant papers and points.

https://saif0524.github.io/code-smell-detection-survey/

Saif:

Hi group,

Milestone: Complete Literature Survey - Dec 5

• Action items from last week–

• Since Friday, I have outlined 10 more papers for the literature survey

• Action Items for next week:

• End of November 15: Complete summaries for all 50 papers.

• End of November 19: Write a clear outline for the review on shared overleaf project. each section linked to relevant papers and points.

Saif:

Hi group,

Milestone: Complete Literature Survey - Dec 5

Action items–

Last week, I have summarized these four papers for the literature survey

• Applying Machine Learning to Customized Smell Detection: AMulti-Project Study

• Predicting Code Smells and Analysis of Predictions: Using Machine Learning Techniques and Software Metrics

• A machine-learning based ensemble method for anti-patterns detection

• A large empirical assessment of the role of data balancing in machine-learning-based code smell detection

This Friday, I will brief my current findings from the literature search.

Replies:

Dr. Lei:

if i remember correctly, i suggested you make a schedule towards the paper submission. can you share the schedule?

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Saif:

Hi group,

Milestone: Complete Literature Survey - Dec 5

Action items

Last week, I have summarized these three papers for the literature survey

• Python code smell detection using machine learning

• An Empirical Study of Code Smells in Transformer-based Code Generation Techniques

• Voting Heterogeneous Ensemble for Code Smell Detection

I will present my current findings to the group on the following Friday (11/8).

Saif:

Milestone: Complete a Survey paper - Dec 5 (venue yet to decide)

Action items -

Last week I have read these four papers from my literature search and jotted down the problems they are addressing, key insights and inspiration for solution, steps they are taking toward the solution, and the inputs and outputs generated from their approach

1. Detection Bad Code Smells By Using Deep Machine Learning Approaches

1. Improving performance with hybrid feature selection and ensemble machine learning techniques for code smell detection

1. MARS: Detecting brain class/method code smell based on metric–attention mechanism and residual network

1. Code Smell Detection using Hybrid Machine Learning Algorithms

Replies:

Dr. Lei:

please break down this milestone and have some intermediate deliverables, so that you can gauge the intermediate progress.

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Saif:

Milestone: Complete a Survey paper - Dec 5 (venue yet to decide)

– for research I have updated the list for my literature search.

– I have documented papers that use LLM approach for source code smell / code summarization task

– I was mostly working on my course projects last week

– I have been grading Midterm papers and an assignment for TA work

Saif:

Hello everyone,

Broad Milestone: Complete Code smell detection project by Thanksgiving (Nov 26)

Complete grouping the papers Sep 29 - Partially completed

Present the findings of the existing literature Oct 11th. for this one I am preparing a presentation to show the findings of LLM approaches in codesmell detection.

Action Items from last week:

1. Completed grouping papers for LLM related works.

1. New Idea presentation: presented FedCSD paper. General Feedback: try to avoid general papers (mostly from IEEE Access) for new Idea presentation.

Replies:

Dr. Lei:

For a milestone to be meaningful, it must have clear deliverables and an objective way to check whether it is completed. Speaking of a research project being completed, i would expect a research paper to be submitted.

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Dr. Lei:

also for your project to make real progress, i want you to make a proposal to the group. in the proposal, please clearly specify what problem you are trying to address, in terms of input/output, what are the technical challenges, and what are your ideas to address the challenges, and how your idea would compare to existing work. also you must break down a project into smaller tasks and put a target date on each small task.

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Saif:

Thank you for the feedback Professor.

I’ll revise the milestone to clearly define goals and to break it into smaller tasks.

Our plan was to aim for survey paper. Should I target for a conference in December/January deadline?

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Dr. Lei:

typically a survey paper is published in a journal paper. not many conferences accept survey papers. one target journal is ACM Computing Surveys. journal submission can be made anytime, i.e., there is no specific deadlines. i suggest you target the end of this semester.

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Saif:

Sounds good. I will adjust the milestone deadline accordingly.

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No update was provided for the week ending 2024-10-02.

Saif:

Hello everyone,

Broad Milestone: Complete Code smell detection project by Thanksgiving (Nov 26)

• Complete grouping the papersSep 29 (original date was Sep 3)

• Present the findings of the existing literature Oct 11th

Action Items from last week:

I am still working categorization of the papers for the literature review

No update was provided for the week ending 2024-09-17.

No update was provided for the week ending 2024-09-13.