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(EvALign-ICL) Beyond Task Performance: Evaluating and Reducing the Flaws of Large Multimodal Models with In-Context Learning

EvALign-ICL is an evaluation framework, based on academic and public datasets, for large multimodal models (LMMs). Currently, the evaluation of LMMs spans 5 different axes; object hallucinations, answer abstention, compositionality, explainabilty and instruction following. We propose 3 setups; zero-shot, in-context learning (ICL) and different variants of ICL (X-ICL).

Abstract

Following the success of Large Language Models (LLMs), Large Multimodal Models (LMMs), such as the Flamingo model and its subsequent competitors, have started to emerge as natural steps towards generalist agents. However, interacting with recent LMMs reveals major limitations that are hardly captured by the current evaluation benchmarks. Indeed, task performances (e.g., VQA accuracy) alone do not provide enough clues to understand their real capabilities, limitations, and to which extent such models are aligned to human expectations.

To refine our understanding of those flaws, we deviate from the current evaluation paradigm, and (1) evaluate 10 recent open-source LMMs from 3B up to 80B parameter scale, on 5 different axes; hallucinations, abstention, compositionality, explainability and instruction following. Our evaluation on these axes reveals major flaws in LMMs.

While the current go-to solution to align these models is based on training, such as instruction tuning or RLHF, we rather (2) explore the training-free in-context learning (ICL) as a solution, and study how it affects these limitations. Based on our ICL study, (3) we push ICL further and propose new multimodal ICL variants such as; Multitask-ICL, Chain-of-Hindsight-ICL, and Self-Correcting-ICL.

Our findings are as follows. (1) Despite their success, LMMs have flaws that remain unsolved with scaling alone. (2) The effect of ICL on LMMs flaws is nuanced; despite its effectiveness for improved explainability, answer abstention, ICL only slightly improves instruction following, does not improve compositional abilities, and actually even amplifies hallucinations. (3) The proposed ICL variants are promising as post-hoc approaches to efficiently tackle some of those flaws.

News

• [2024.1.22]: The full code is released.
• [2024.1.16]: The paper is accepted at ICLR 2024 !
• [2023.11.10]: We added experiments for the 80B-parameters IDEFICS models, evaluation on SugarCREPE, and GPT-4 evaluation for LlaVA.
• [2023.10.1]: The code will be released soon. For now you can install the libraries, download the models and prepare the datasets !

Table of Contents

• Installation
• Usage
• Evaluation
• X-ICL
• Extending EvALign-ICL
• Acknowledgments
• Citing

Installation

Our installation follow the installation of OpenFlamingo. To run IDEFICS you need a recent transformers library. The main libraries that we use are as follows:

torch == 2.0.1
torchvision == 0.15.2
transformers == 4.28.0
accelerate == 0.21.0

Usage

To do inference on custom images and see your results you can check OpenFlamingo and IDEFICS

Evaluation on the EvALign-ICL benchmark

Technical notes

The scripts to launch the evaluation can be found here scripts/.

Scripts containing of or idefics in their name are used to evaluate OF or IDEFICS models respectively. To evaluate a particular model, uncomment the corresponding lines in the script.

The MODE is used to slect the evaluation setup: ICL, MT-ICL (MultiTask-ICL), SC-ICL (Self-Correcting-ICL) or CoH-ICL (Chain-of-Hindsight-ICL).

If you want to test only in zero-shot you can set the number of shots to 0. We use one GPU of 32GB, sometimes you need bigger GPU (40GB) to evaluate the biggest models with 32 shots. For IDEFICS it is faster to use A100 GPUs as it is evaluated with BFloat16.

For OF models, we download the models and read them from our local repo (as we don't have access to internet on the cluster).

Object Hallucinations

Dataset

• Download: download COCO captioning dataset (we use val2014). Also you need the synonyms for the CHAIR metrics.

• Format: the data['annotations'] (data loaded from captions_val2014_objs_vinvl.json) is captions_val2014.json augmented with objects list for each image for example:

{'image_id': 179765,
 'id': 182,
 'caption': 'A Honda motorcycle parked in a grass driveway',
 'objects': ['tire', 'wheel', 'land vehicle', 'tree', 'vehicle', 'motorcycle']}

You need objects only if you want to test MT-ICL. The objects are extracted with VinVL object detection model and can be downloaded from here. The ids are the coco image ids.

Evaluation

Launch scripts scripts/object_hallucinations/.

Evaluation setups selected with MODE: ICL (baseline) or MT-ICL (caption_objects).

Object hallucination. CIDEr (↑) for captioning and CHAIRs (↓) for hallucination on COCO dataset.

Answer Abstention

Dataset

• Download: download the TDIUC dataset.
• Format: it has similar format to VQAv2. the data['annotations'] (data from TDIUC/Annotations/mscoco_val2014_annotations.json) contains items in the following format (all types including the absurd one):

{'question_type': 'absurd',
 'image_id': 448308,
 'ans_source': 'generation',
 'answers': [{'answer': 'doesnotapply',
   'answer_confidence': 'yes',
   'answer_id': 1}],
 'question_id': 10370276}

Evaluation

Launch scripts scripts/abstention/.

Evaluation setups selected with MODE: ICL (baseline), MT-ICL (vqa_abstention) and SC-ICL.

For SC-ICL:

• first launch with vqa_abstentiononly to classify the questions,
• then with correct_shot/correct_32shot (for SC-ICL and SC-ICL 32shots) while setting PREVIOUS_PREDS to the path generated from the classification path/tdiuc_unans_results_seed.json. For simplicity we do the classification before the correction.

Abstention. Overall VQA accuracy (↑) and abstention F1-score (↑) on TDIUC dataset.

Compositionality

CREPE

Dataset

• Download: download the CREPE dataset and images from Visual Genome
• Format: the data from syst_vg_hard_negs_seen_compounds_in_laion.json contain items in the following format:

{'region_id': '374',
 'caption': 'a red brick paved sidewalk',
 'x': '346',
 'y': '329',
 'width': '452',
 'height': '269',
 'valid_hard_negs_comp': ['a red mall and blue sidewalk',
  'a red alley and black sidewalk',
  'a red alley and yellow sidewalk'],
 'valid_hard_negs_atom': ['a brown brick paved sidewalk',
  'a white brick paved sidewalk',],
 'image_id': '2',
 'valid_hard_negs_combined': ['a brown brick paved sidewalk',
  'a white brick paved sidewalk',
  'a purple brick paved sidewalk','],
 'image_path': 'VG_100K/2.jpg',
 'objects': [{'object': 'walk sign'},
  {'object': 'backpack'},]
 'image_w': 800,
 'image_h': 600}

Objects are used for MT-ICL. Objects are from the annotations of visual genome (path/visual_genome/annotations/objects.json).

Evaluation

Launch scripts in scripts/compositionality/.

Evaluation setups selected with MODE:

• For ITM: ICL (itm) and MT-ICL (itm_objects)

• For ITS: ICL (baseline).

To evaluate on systematicity, you need to set COMPOS to sys and NEG_TYPE to ; atom (HN-Atom), comp (HN-Comp), combined (HN-Atom + HN-Comp). For the baseline (the negative is a random caption sampled from the COCO dataset) you need to set COMPOS to normal and NEG_TYPE to baseline.

Compositionality. Models are evaluated on the CREPE benchmark with the ITM task. We evaluate on systematicity, we consider 2 types of negative captions: HN-Atom (replacing atoms, such as objects, attributes, or relations with atomic foils) and HN-Comp (composing two negative captions constructed with HN-Atom). We noticed similar observations with productivity

SugarCREPE

The evaluation is similar as before for CREPE, with the following modifictions:

Dataset

• Download: download the SugarCREPE.

Evaluation

• Launch scripts in scripts/compositionality/. eval_itm_idefics_sugarcrepe.sh for IDEFICS and eval_itm_of_sugarcrepe.sh for OpenFlamingo.

Evaluation on SugarCREPE. Evaluaation on each type can be found in the paper.

Explainability

Dataset

• Download: download VQA-X. It uses images from coco.
• Format: data['annotations'] from vqaxtest_v2_mscoco_val2014_annotations.json contains items of the following format:

{'question_type': 'what is this',
 'multiple_choice_answer': 'shower',
 'answers': [{'answer': 'shower', 'answer_confidence': 'yes', 'answer_id': 1},
  {'answer': 'shower', 'answer_confidence': 'yes', 'answer_id': 2},
  {'answer': 'shower', 'answer_confidence': 'yes', 'answer_id': 3},
  {'answer': 'shower', 'answer_confidence': 'yes', 'answer_id': 4},
  {'answer': 'shower', 'answer_confidence': 'maybe', 'answer_id': 5},
  {'answer': 'shower', 'answer_confidence': 'yes', 'answer_id': 6},
  {'answer': 'shower', 'answer_confidence': 'yes', 'answer_id': 7},
  {'answer': 'shower', 'answer_confidence': 'yes', 'answer_id': 8},
  {'answer': 'shower', 'answer_confidence': 'yes', 'answer_id': 9},
  {'answer': 'shower', 'answer_confidence': 'yes', 'answer_id': 10}],
 'image_id': 262284,
 'answer_type': 'other',
 'question_id': 262284001,
 'explanation': ['it has a shower head hanging inside of it.',
  'there is a shower head',
  'There is a faucet and a bathtub.'],
 'question': {'image_id': 262284,
  'question': 'What is this?',
  'question_id': 262284001}}

Evaluation

Launch scripts scripts/explainability/.

Evaluation setups selected with MODE:

• For Answer only: ICL (baseline)

• For explain only: ICL (explainonly).

• For answer and explain: MT-ICL (answer_and_explain)

• For CoH-ICL: first explain only generation of the query (explainonly_evalquery), then Coh-ICL (explainonly_contrastive_prevqout_32shotvsgt) with PREV_Q_OUT set to the generated explanation (vqax_results_seed.json).

Explainability. Models are asked to generate an explanation for image, question and answer triplets from the VQA-X dataset.

Datasets

We describe the datasets that we use in the following sections. We don't share the datasets due to licenses issues, and each one should be downloaded and reformatted as described below.

Instruction Following

Dataset

• Download: download LLaVA from here. It uses COCO images.
• Format: for example the llava_complex_reasoning_77k.json contains items with the following format:

{'global_image_id': 'coco.215677',
 'image_path': 'coco/train2014/COCO_train2014_000000215677.jpg',
 'anns_id': 'lllava_complex_reasoning_77k.215677',
 'question': 'What skill set might someone need to perform such a frisbee trick?',
 'answer': 'To perform the frisbee trick shown in the image, where the man is passing a frisbee between or underneath his legs, a person would need a combination of skills. These skills include good hand-eye coordination, agility, balance, flexibility, and dexterity. Additionally, the ability to throw and catch the frisbee accurately while maintaining control of bodily movements would also be essential. To perfect the trick, practicing these skills and building up muscle memory through repetition would be beneficial.',
 'image_id': 215677}

Other instruction types follow similar format.

We reformat the test questions/answers and put them in data/llava.

Evaluation

Launch scripts scripts/instruction_following/.

Evaluation setups selected with MODE, currently we only evaluate with ICL on LlaVA complex questions (llava_complex_questions), detailed descriptions (llava_detail_questions), conversations (llava_conv_questions or llava_conv_dialog_questions)

For zero-shot (not a la Flamingo) we set the num of shots to 1 (these are the results illustrated in the paper when we refer to ZS instruction following)

> Instruction following. Qualitative evaluation results of IDEFICS and OFv2-9B on the LlaVA benchmark on 3 types of instructions (from left to right): detailed descriptions, complex questions and conversations.

Evaluation with GPT4

To evaluate with GPT-4 we follow the same setup as LlAVA. After generating the responses with scripts/instruction_following/, use open_flamingo/eval/llava/merge_result_files.py to merge the output files, then open_flamingo/eval/llava/eval_llava.sh to evaluate with GPT$. Don't forget to set your OPENAI_API_KEY variable in therminal.

Evaluation on LLaVA-all with GPT-4.

X-ICL

We push ICL further and propose new improved variants to address some of LMMs limitations. We explore 3 variants illustrated below; CoH-ICL, SC-ICL and MT-ICL.

CoH-ICL to improve model explainability. The context consists of; an image, question, answer, human annotation as the good response, and previous model's generation (with ICL 32-shot) as the bad response.

SC-ICL for answer abstention, we first ask the model the question using ICL. Then, for each question, we ask the model to decide whether the question is answerable based on the image or not. In case the model recognizes that the question is not answerable, the previous answer is ignored and replaced with an abstention keyword. The correction is with 32-shot in this step 2.

MT-ICL for abstention. The main task is to answer the question and the second auxiliary task is to decide whether the question is relevant to the image or not.

Acknowledgments

The code is mainly based on the following previous work: OpenFlamingo and IDEFICS.

The authors would like to thank Hugo Laurençon for fruitful discussions.

Citing

If you found this repository useful, you can cite it as:

      @article{shukor2023beyond,
        title={Beyond Task Performance: Evaluating and Reducing the Flaws of Large Multimodal Models with In-Context Learning},
        author={Shukor, Mustafa and Rame, Alexandre and Dancette, Corentin and and Cord, Matthieu},
        journal={arXiv preprint arXiv:2307.16184},
        year={2023}
      }