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GPT-3.5CHATGPTGPT-4
TaskBase+SELF-REFINEBase+SELF-REFINEBase+SELF-REFINE
Sentiment Reversal8.830.4 (↑21.6)11.443.2 (↑31.8)3.836.2 (↑32.4)
Dialogue Response36.463.6 (127.2)40.159.9 (↑19.8)25.474.6 (↑49.2)
Code Optimization14.823.0 (↑8.2)23.927.5 (13.6)27.336.0 (↑8.7)
Code Readability37.451.3 (↑13.9)27.763.1 (↑35.4)27.456.2 (↑28.8)
Math Reasoning64.164.1 (0)74.875.0 (↑0.2)92.993.1 (10.2)
Acronym Generation41.656.4 (↑14.8)27.237.2 (↑10.0)30.456.0 (↑25.6)
Constrained Generation16.039.7 (↑23.7)2.7533.5 (130.7)4.461.3 (156.9)
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TaskSELF-REFINE feedbackGeneric feedbackNo feedback
Code Optimization27.526.024.8
Sentiment Reversal43.231.20
Acronym Generation56.454.048.0
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Tasky0Y192y3
C.Opt.22.027.027.928.8
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Supervision- free refinerSupervision- free feedbackMulti-aspect feedbackIterative
Learned refiners: PEER (Schick et al., 2022b), Self-critique (Saunders et al.,2022b), CodeRL (Le et al.,2022b), Self-correction (Welleck et al., 2022).×□orX×√orX
Prompted refiners: Augmenter (Peng et al.. 2023), Re³ (Yang et al., 2022), Reflexion (Shinn et al.,2023).√□orXXX
SELF-REFINE (this work)√√√√
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Task and DescriptionSample one iteration of FEEDBACK-REFINE
SentimentReversalx: The food was fantastic..."
Rewrite reviews to reverse sentiment. Dataset: (Zhang et al., 2015) 1000 review pas-yt: The food was disappointing...” fb:Increase negative sentiment
sages Dialogue Response GenerationYt+1: The food was utterly terrible.." x: What's the best way to cook pasta?"
Produce rich conversational responses.Yt: The best way to cook pasta is to..."
Dataset: (Mehri and Eskenazi, 202O) 372 conv.fb: Make response relevant, engaging, safe
Yt+1: Boil water, add salt, and cook pasta..."
Code Optimization Enhance Python code efficiencyx:Nested loop for matrix product
Dataset: (Madaan et al.,2023): 1000 programsYt: NumPy dot product function
fb:Improve time complexity
Yt+1: Use NumPy's optimized matmul function
CodeReadabilityImprovement Refactor Python code for readability.x:Unclear variable names,no comments
Dataset: (Puri et al., 2021) 300 programs*Yt: Descriptive names, comments fb:Enhance variable naming;add comments
Yt+1: Clear variables,meaningful comments
Math Reasoningx: Olivia has $23,buys 5 bagels at $3 each"
Solve math reasoning problems.Yt:Solution in Python
Dataset: (Cobbe et al., 2021) 1319 questionsfb: Show step-by-step solution
Yt+1: Solution with detailed explanation
Acronym Generation Generate acronyms for a given titlex : Radio Detecting and Ranging"
Dataset:(Appendix T) 25O acronymsYt:RDR fb :be context relevant; easy pronunciation
Yt+1: RADAR"
Constrained Generation
x:beach,vacation,relaxation
Generate sentences with given keywords.Yt: During our beach vacation..
Dataset: (Lin et al., 2020) 200 samplesfb:Include keywords;maintain coherence
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MethodPrimary Noveltyzero/few shot improvementmulti aspect criticsNL feedback with er- ror localizationiterative framework
RLHF (Stiennon et al., 2020) Rainier RL (Liu et al., 2022) QUARK RL (Lu et al., 2022) Code RL (Le et al., 2022a)optimize for human preference RL to generate knowledge quantization to edit generations actor critic RL for code im-Xtrained on feedback Xtrained on end task × trained on end task Xtrained on end taskX single (human) X single(accuracy) × single(scalar score) × single(unit tests)√(not self gen.) X(knowl. only) X(dense signal) X(dense signal)×
DrRepair (Yasunaga and Liang, 2020) PEER (Schick et al., 2022b)provement Compiler feedback to itera- tively repair doc. edit trained on wiki editsXtrained semi sup. Xtrained on editsX single(compiler msg) Xsingle(accuracy)(not self gen.) (not self gen.)√ √
Self critique (Saunders et al., 2022a) Self-correct (Welleck et al., 2022) Const. AI (Bai et al., 2022b)few shot critique generation novel training of a corrector train RL4F on automat (cri- tique,revision) pairXfeedback training trained on end taskX single(human) single (task specific) (fixed set)(self gen.) √(limited setting) √× (limited setting) ×
Self-ask (Press et al., 2022)ask followup ques when in- terim ans correct;final wrongfew shotX noneX(none)×
GPT3 score (Fu et al., 2023)GPT can score generations with instruction√few shotX single(single utility fn)X(none)×
Augmenter (Peng et al., 2023)factuality feedback from exter-few shotX single(factuality)(self gen.)√
Re3 (Yang et al., 2022)nal KBs ~ours: but one domain,√few shot(trained critics)(not self gen.)√
SELF-REFINEtrained critics fewshot iterative multi aspect NL fbfew shot√multiple(few shot critics)(self gen.)√
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TaskSELF-REFINE（%)Direct (%)Either (%)
SentimentReversal75.0021.433.57
Acronym Generation44.5912.1643.24
Dialogue Response Generation47.5819.6632.76
Code Readability Improvement50.03.047.0
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TaskBaseSELF-REFINE (zerO-shot)
Acronym Generation16.6%44.8% (↑ 28.2%)
Constrained Generation4.0%47.0% (↑ 43.0%)
Sentiment Reversal4.4%71.4% (↑ 67.0%)
Math Reasoning22.1%59.0% (↑ 36.9%)
Dialogue Response Generation23.0%48.8% (↑ 25.8%)
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TaskBase model+Self-RefineGain
Date Understanding62.066.84.8
Geometric Shapes17.620.02.4
Logical Deduction (seven objects)43.245.22.0
Multi-Step Arithmetic61.664.02.4
Tracking Shuffled Objects (seven objects)31.636.04.4
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Task% Base% SELF-REFINE
Dialogue Response Generation30.664.7 (↑34.1)
SentimentReversal10.669.2 (158.6)
Acronym Generation32.049.2 (↑17.2)
Code readability37.060.0 (↑23.0)
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gen_sys_template = "You are a helpful assistant that responds with only a pythonFunction to Generate an Answer
「 program." gen_user_template = """Write a python function that solves the given question and
→returns the result.
Always store the final result in a variable called ‘result`，and always include
→`return result` as your last statement.
# Question: {question}
# solution using Python: """
# Function to Get Feedback on the Answer
fb_sys_template = "You are a helpful assistant that provides feedback on the
correctness of python programs." fb_user_template = """Question: {question}
{prediction}
# There may be an error in the code above because of lack of understanding of the
「question.
To find the error，go through semantically complete blocks of the code，and check
→ if everything looks good.
Errors could include:
- Logical issues
- Lack of understanding of the question
- Missing ‘return result`
Share your evaluation in the format:
Evaluation: <your evaluation here>.
If everything looks good, return 'Evaluation: correct '"""
# Function to Fix the Error
refine_sys_template = "You are a helpful assistant that responds with only a
→ python program."
refine_user_template = """Question: {question}
{prediction}
# There is an error in the code above. The following is the feedback on the code:
{feedback}
Fix the error in the python function given the feedback above. The python function should return the final answer.""
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f"""Which response is better given this context: {context}? Response A: {response_a}
Response B: {response_b}.
Pick your answer from ['Response A'， 'Response B'，'both'， 'neither']. Generate a
→ short explanation for your choice first. Then，generate 'The better response
is A' or' 'The better response is B' or 'The better response is both' or' 'The
better response is neither'
Format: <explanation> <answer> STOP
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f"""Title: {title}
Acronym A: {acronym_a}
Acronym B: {acronym_b}
Pick the better acronym for the given title. The acronyms should be compared based on the following criteria:
* Ease of pronunciation.
* Ease of spelling.
* Relation to title.
* Positive connotation.
Generate your answer in the following format:
<Short explanation>. The better acronym is A OR The better acronym is B OR The
acronyms are equally good OR Neither acronym is good. STOP.
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MethodSolve Rate
Chen et al. (2021)CODEX71.3
Cobbe et al. (2021) Wei et al. (2022)OpenAI 6B20.0
CoTw/ CODEX65.6
Gao et al. (2022)PaL w/ CODEX72.0
PaL w/ GPT-352.0
PaL w/ GPT-3.556.8
PaL W/CHATGPT74.2
PaL W/GPT-493.3
Welleck et al. (2022)Self-Correct w/ GPT-345.9
Self-Correct (fine-tuned)24.3
This workSELF-REFINE W/ GPT-355.7
SELF-REFINE W/ GPT-3.562.4
SELF-REFINE W/ CHATGPT75.1
SELF-REFINEW/GPT-4 SELF-REFINE w/ CODEX (Oracle Feedback)94.5
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Method%OPT
Puri et al. (2021)Human References38.2
OpenAI Models: OpenAI (2022, 2023)CODEX GPT-3.5 CHATGPT9.7 14.8 22.2 27.3
Nijkamp et al. (2022)GPT-4 CODEGEN-16B1.1
Berger et al. (2022)SCALENE SCALENE (BEST@16) SCALENE (BEST@32)1.4 12.6
Madaan et al. (2023)PIE-2B PIE-2B (BEST@16) PIE-2B (BEST@32)19.6 4.4 21.1 26.3
PIE-16B PIE-16B (BEST@ 16) PIE-16B (BEST@32) PIE-FeW-shot (BEST@16) PIE-Few-shot (BEST@32)4.4 22.4 26.6 35.2 38.3
This workSELF-REFINE W/ CODEX SELF-REFINE W/ GPT-3.5 SELF-REFINEV W/ CHATGPT SELF-REFINE W/ GPT-415.6 23.0 26.7 36.0
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Method%Readable Variables
Chen et al. (2021)CODEX37.4
This workSELF-REFINE W/CODEX51.3
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TaskBaseSelf-RefineEqually Good
Acronym Generation22.3053.0822.30
Sentiment Reversal13.260.826
Dialogue Response Generation11.220.454.6
Math Reasoning37.637.8 (41 with Oracle)N/A
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GPT-3.5CHATGPTGPT-4
TaskBase+SELF-REFINEBase+SELF-REFINEBase+SELF-REFINE
Math Reasoning64.164.1(0)74.875.0 (10.2)92.993.1 (10.2)
Math Reasoning (Oracle)64.0668.9 (↑4.8)74.876.2 (↑1.4)92.993.8 (10.7)
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IterationAcronymPronunciationPron. (5)Spell. (5)Rel. (5)Pos. Con. (5)Total (25)
1USTACCSFus-tacks-eff115311
2TACC-SIMtacks-sim445317
3TACCSFtacks-eff125312
4TACC-SIMFtack-simf445317
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Error TypeOccurrenceFeedbackRefinement
Not-Robust10%Specific: The response is specific and provides spe- cific details or examples. 3/3Response: That's pretty funny!It's understand- able that you didn't do well on the test. I'm sure you'll do much bet- ter when you take History next semester
Ignores feedback25%Informative: The re- sponse doesn't provide any new information. 1/3Response: That's so kind and generous of you！ I would love to hear more about the podcasts you enjoy. Do you have any favorites you can recom- mend?
Introduces new problem20%Informative:3/3Response: :That would be a disaster!Luckily, my coworkers know bet- ter than to drink while they work. New Informa- tive score: 2/3
Robusttobadfeedback60%Informative: The re- sponse does not provide any information. 1/3Response: Wow, 25 peo- ple! That must have been an amazing experience. Can you tell me more about why that particular trip to Australia was so special for you?
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Incorrect feedback25%Informative: The response provides some encourage- ment and reassurance, and additional information.3/3Response: ：That's hilarious! Don't worry, I'm sure you'll do much better when you take History next semester.Are you looking forward to it?
Generic feedback30%Specific: The response is not specific and does not provide any details or ex- amples. 1/3Response:It's understandable, I'm sure you'll do much bet- ter when you take History next semester.Maybe you can use your knowledge of Christo- pher Columbus to ace your next test! Are you looking for- ward to it?
Incorrect Scoring10%Informative: The response provides a supportive and understanding message, which is informative to the user. 2/3Response:I'm sorry to hear.. when Christopher Columbus didn't even fight in the Civil War!I'm sure you'll do much better.... Are you looking for- ward to it?
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TaskGPT-3.5CHATGPTGPT-4
Base+SELF-REFINEBase+SELF-REFINEBase+SELF-REFINE
Sentiment Reversal8.8 ± 2.0530.4 ±3.61*11.4 ± 2.3443.2 ±3.98*3.8±1.2836.2 ±3.82*
Dialogue Response36.4 ± 6.1463.6± 6.62*40.1 ± 6.3359.9 ± 6.67*25.4 ± 5.3674.6± 6.22*
Code Optimization14.8 ± 2.6623.0±3.25*23.9 ±3.3027.5±3.4927.3 ±3.4836.0±3.81*
Code Readability37.4 ± 6.8651.3 ± 7.3927.7 ± 6.1363.1± 7.40*27.4 ± 6.1056.2 ± 7.45*
Math Reasoning64.1 ± 3.4764.1 ± 3.4774.8 ±3.2075.0±3.2092.9 ± 2.0593.1± 2.03
Acronym Gen.41.6 ± 7.7256.4±8.1527.2 ±6.6037.2 ± 7.4630.4 ± 6.9256.0±8.15*
Constrained Gen.28.0 ± 7.3837.0±8.2644.0 ±8.7267.0±9.00*15.0 ± 5.3845.0±8.77*
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Criteriaoutput from GPT3: STSLWNoutput from SELF-REFINE: Seq2Seq
Ease of pronunciationPronounced as ess-tee-ess-ell-double- you-enn which is very dificult.Pronounced as seq-two-seq which is easy.
Ease of spellingVery difficult to spell.Easy to spell.
Relation to titleNo relation to the title.Mentions sequence which is somewhat related to the title.
Positive connotationMeaningless acronym.Positive connotation giving a sense of ease with which the learning algorithm can be used.
Well-knownNot a well-known acronym.Close to the word sequence which is a well- known word.
Total score5/2520/25
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Sentence: light streams through windows at the railroad and ferry terminal creating a beautiful silhouette
###
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Sentence: A room with a couch， chairs and art hanging on the wall.
###
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Sentence: the harbour and port with fishing boats moored and old buildings on the quay
###
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Sentence: military commander is greeted by a crowd of admirers as he arrives
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###
Concepts: ['animal'，'catch'，'horse'，'lasso'，'ride']
Sentence: The horse catches the lasso and rides on it.
what concepts from the concept list are missing from the sentence?
Concept Feedback: animal
Any feedback on commonsense?
Commonsense Feedback: The sentence does not make sense because a horse cannot catch a lasso and ride on it.
Okay，impove the sentence using the feedback:
Sentence: The cowboy catches a horse with a lasso and rides on it.
what concepts from the concept list are missing from the sentence?
Concept Feedback: animal
Any feedback on commonsense?
Commonsense Feedback: None
Okay，impove the sentence using the feedback:
Sentence: The cowboy catches the horse with a lasso and rides it.
what concepts from the concept list are missing from the sentence?
Concept Feedback: None
Any feedback on commonsense?
Commonsense Feedback: None
###
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