[PRE REVIEW]: Multiple Choice Reasoning via. Process of Elimination using Multi-Modal Models

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Submitting author: !--author-handle-->@hellokayas<!--end-author-handle-- (Sayak Chakrabarty) Repository: https://github.com/souradipp76/MM-PoE Branch with paper.md (empty if default branch): main Version: v0.1.2 Editor: Pending Reviewers: Pending Managing EiC: Chris Vernon

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Software report:

github.com/AlDanial/cloc v 1.90  T=0.06 s (935.1 files/s, 168692.6 lines/s)
-------------------------------------------------------------------------------
Language                     files          blank        comment           code
-------------------------------------------------------------------------------
Python                          18            712            578           6738
Bourne Shell                    14             59             68            646
CSV                             10              0              0            495
Markdown                         9            228              0            479
TeX                              1             39              0            271
YAML                             6             21             13            173
make                             1             16              4            104
-------------------------------------------------------------------------------
SUM:                            59           1075            663           8906
-------------------------------------------------------------------------------

Commit count by author:

   102  Souradip Pal
    13  hellokayas
     7  Sayak Chakrabarty
     5  dependabot[bot]
     1  souradipp76

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Paper file info:

📄 Wordcount for paper.md is 2261

✅ The paper includes a Statement of need section

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License info:

✅ License found: Apache License 2.0 (Valid open source OSI approved license)

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Reference check summary (note 'MISSING' DOIs are suggestions that need verification):

✅ OK DOIs

- None

🟡 SKIP DOIs

- No DOI given, and none found for title: Language models are few-shot learners
- No DOI given, and none found for title: On the consistency of maximum likelihood estimatio...
- No DOI given, and none found for title: Learning by the process of elimination
- No DOI given, and none found for title: Large language models are zero-shot reasoners
- No DOI given, and none found for title: : A Dynamically Adaptive Defense to a Novel Attack...
- No DOI given, and none found for title: Training language models to follow instructions wi...
- No DOI given, and none found for title: Leveraging large language models for multiple choi...
- No DOI given, and none found for title: Beyond the imitation game: Quantifying and extrapo...
- No DOI given, and none found for title: CommonsenseQA: A question answering challenge targ...
- No DOI given, and none found for title: Llama: Open and efficient foundation language mode...
- No DOI given, and none found for title: PINTO: Faithful language reasoning using prompt-ge...
- No DOI given, and none found for title: Self-consistency improves chain of thought reasoni...
- No DOI given, and none found for title: Chain of thought prompting elicits reasoning in la...
- No DOI given, and none found for title: Tree of thoughts: Deliberate problem solving with ...
- No DOI given, and none found for title: Satisfiability-aided language models using declara...
- No DOI given, and none found for title: Calibrate before use: Improving few-shot performan...
- No DOI given, and none found for title: BLIP2: Opt Model
- No DOI given, and none found for title: BLIP2: FLAN-T5-XL Model
- No DOI given, and none found for title: InstructBLIP: Vicuna Model
- No DOI given, and none found for title: GIT: Base VQA Model
- No DOI given, and none found for title: GIT: Base TextVQA Model
- No DOI given, and none found for title: PaliGemma: Fine-Tuned Science QA Model
- No DOI given, and none found for title: PaliGemma: Fine-Tuned VQAV2 Model
- No DOI given, and none found for title: PaliGemma: Fine-Tuned AI2D Model
- No DOI given, and none found for title: Idefics2: 8b Model
- No DOI given, and none found for title: VQA: Visual Question Answering
- No DOI given, and none found for title: Learn to Explain: Multimodal Reasoning via Thought...

❌ MISSING DOIs

- 10.18653/v1/2020.emnlp-main.49 may be a valid DOI for title: Event extraction by answering (almost) natural que...
- 10.18653/v1/2023.acl-long.783 may be a valid DOI for title: Mitigating label biases for in-context learning
- 10.18653/v1/2023.emnlp-main.273 may be a valid DOI for title: POE: Process of Elimination for Multiple Choice Re...
- 10.18653/v1/2021.emnlp-main.564 may be a valid DOI for title: Surface form competition: Why the highest probabil...
- 10.18653/v1/2023.acl-long.129 may be a valid DOI for title: Z-ICL: Zero-shot in-context learning with pseudo-d...
- 10.18653/v1/2022.acl-long.565 may be a valid DOI for title: Coherence boosting: When your pretrained language ...
- 10.18653/v1/2022.acl-long.365 may be a valid DOI for title: Noisy channel language model prompting for few-sho...
- 10.18653/v1/2020.acl-main.441 may be a valid DOI for title: Adversarial NLI: A new benchmark for natural langu...
- 10.18653/v1/d19-1454 may be a valid DOI for title: Social IQa: Commonsense reasoning about social int...
- 10.18653/v1/2022.findings-acl.65 may be a valid DOI for title: Debiasing event understanding for visual commonsen...
- 10.18653/v1/2023.findings-emnlp.811 may be a valid DOI for title: Automatic prompt augmentation and selection with c...
- 10.18653/v1/2023.findings-acl.824 may be a valid DOI for title: Challenging BIG-bench tasks and whether chain-of-t...
- 10.1007/978-3-319-46493-0_15 may be a valid DOI for title: A Diagram is Worth a Dozen Images

❌ INVALID DOIs

- None

[editorialbot]

:point_right::page_facing_up: Download article proof :page_facing_up: View article proof on GitHub :page_facing_up: :point_left:

[editorialbot]

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