Handling the Follow-up Question: Conversational Explanations for Image Classification
Authors: TONG ZHANG, Mengao Zhang, X. Jessie Yang, Boyang Li


Reviewer paper summaries: * The paper as a tldr aims to aid Explainable AI by building a conversational explanation system.
* The system is proposed to be built using synthetic data.  
  * The paper builds this synthetic data mitigating the challenges of data diversity and hallucination.
  * Data diversity: tackled with a `repetition penalty`
  * Hallucination: tackled with an explicit domain specific hallucination detector
* The data is used to finetuned a LLaVA-1.5 model and then evaluated via both automated and human evaluations as a conversational explanation system.
Summary of strengths: * The papers correctly identifies the limitation of synthetic data and proposes methods to tackle them through a repetition penalty setup and hallucination detection system.
* A custom human evaluation system is designed for the task, given the limitation of automated metrics for the task, this is an important aspect.
Summary of weaknesses: * The e2e method needs a hallucination detection model which is domain/task based and needs custom data collected per domain/task. Making the scalability of this approach difficult.
* Sec 2
  * When a {xi,yi} is dropped by the hallucination detection model, are there concerns if that breaks the context for {xi+1, yi+1}? 
  * Sec 2.2 The data comprehension should be clarified. 
    * What kind of data duplication and diversity checks were applied ?
    * What measures are used to make sure there is no leakage of information between train and validation (or test) sets? 
    * The section mentions 750 incorrect data points sourced from corrupted 500 factually correct sentences. Please clarify where the rest comes from?
    * Hallucination dataset and/or conversation dataset release plan is not discussed.

* Sec 3
  * Please add the #steps used in LoRA finetuning of the LLaVa-1.5 model.
  * Sec 3.2, Evaluation
    * Participants were recruited from 14 majors. Please note what the majors are and volume/major. How the participants-major pairs were distributed over eval buckets (explanation methods and generation methods).
    * Given the evaluation revolves around explainability of ML algorithms, do we have any study on how non-ML background major participants interacted with the system and if the questions asked by them were abstract/easy compared to those with ML majors. Additionally did the distribution of the participants have any effect on the overall result, eg: if we slice the numbers by ML vs non-ML majors, how different are they?
  * Please discuss the shortcomings of BLEU and ROUGE as metrics for this task.
 
* Fig 7, since this is the only exemplar of the "conversation" in the paper, it is unclear why the chat needs all the context (on the left) of the image, the task, answer, etc. The chat seems to ask generic ML knowledge based questions (not strictly related to the context on the left) which should be answerable by most powerful LLMs today. Any study with larger sized LLMs (GPT-4*, Gemini, etc) ?

* Objective evaluations
  * Is this strictly a single choice setting? It is unclear why some of the options are selected in Fig-8, multiple right options per question do seem feasible (eg, the example of the bee or the cat or the husky) which would result in a more apt eval scores.

* Subjective evaluations
  * Please add annotator agreement rates for the questions.
Comments, suggestions and typos: * In Fig 6, the `Explanation method` should be `SHAP`
* Sec 3.2 mentions participants from 14 majors are recruited while 3.3 mentions 19. Please clarify.
Overall assessment scores: 2