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Title: “Tree of Thoughts: Deliberate Problem Solving with Large Language Models (Full Paper Review)”
Transcript: “hi there today I thought we’d just give a quick look at this paper tree of thoughts deliberate problem solving with large language models in summary this paper proposes a sort of decoding technique like a way to use large language models where you don’t just ask them once what they think and try to structure your prompts really smartly like something like Chain of Thought but instead you do an explicit tree search over outputs of the language model with the language model itself valuing these th ree states and therefore being able to Branch off and backtrack and so on it turns out this can help for tasks where such a a pattern of investigating the task is really helpful so the paper proposes the decoding technique and proposes some new tasks where they expect the decoding technique to work really well and then the decoding technique works really well on those tasks make of that as you will it’s an interesting idea I do think it’s like a small step into a new Direction Where We mix langu age models with essentially with programming with algorithms so I’m pretty excited for that and this paper is a step into that direction it’s by people from Princeton University and Google deepmind and will go right into it so the main proposition is this one right here it’s if you simply prompt a language model even if you prompt it really well you say well you are a super helpful helper you’re so helpful you just help me and everything and um I I want you to make to do this task that’s sort of called what they call here input output prompting input output means you specify the task and optionally you may also specify an output format so you may say to a language model hey I have this task right here I want to write an email to my boss please write it in the following format first write Dear boss then write the text in between and at the end write the signature more commonly if you want models to for example output Json as a response you say you know don’t give me a textual answer onl y respond using Json formatted output or even more commonly if you want the model to do a classification task for example you say don’t answer in text answer with one of the following four options right just output the word and then you give the four classes and then you rely on the model only outputting that there are other techniques like restricted decoding that can enforce this stuff and so on but in general input output prompting simply says you ask the language model what you want it to do write a song about a little bird right and please do that in steps so please first make a like an overall plan for the song and output that into individual faults so you would instruct the model not only to have to have a prompt that’s too fat you would instruct it not only to have to have a prompt uh followed by followed by an answer but you would instruct it to Output its thoughts so prom goes here and then you say you know write your thoughts on each line starting with like T and then the mo t turns out and I think that is not there are hypotheses around why that is but it’s I believe it’s not yet fully understood why exactly the Chain of Thought prompting helps but hypotheses are that it gives the model kind of a scratch Pad um like right here in order to write down its thoughts and then the next thoughts can refer back to the previous explain illicit thoughts and not everything has to happen sort of in the weights the second opinion is that it just gives the model sort of a longer possible where you have some sort of a classification task um where you can assemble a majority vote there’s also another concept that’s not mentioned here but that is sort of iterative refinement which comes in later in the paper what you can always do is you can always just append a prompt that says consider your last answer how how might you improve it or consider your last unders do you think it’s correct if not please improve it and you can sort of add that onto any of these techniques so t do that multiple times so three times I go to the language model and just ask it to Output the first thought in the problem solving step just the first one not the whole problem just the first one and those that gives me the three thoughts for example right here one two three then then as a second step so I finish that step as a second step I take the language model and I use it to self-critique all of these thoughts right here with respect to the input prompt so I ask it something like hey what thing it is the case so even if you use the same model to self-critique you’ll get a better signal for the critic than you get for the generation so that’s why it does make sense that after creating something you go and you ask the same model to consider what it just did and how well it fits so the model for example you you’ll give it all of these tasks let me grab you’ll you’ll give it all of these tasks and then you’ll ask it to consider and maybe it will say this one here is really bad this o nue with the ones where I’m more confident then let’s say we we consider the middle one right here so we’ve eliminated the one on the right hand side and we consider it the middle state right here again I sample maybe this time four different thoughts um and again I ask the model um hey what do you think of those four thoughts and now here the the the drawing is a bit wonky I think in that it doesn’t display the algorithm like this this note right here should probably be well I don’t know um but ight as we did over here but we found that none of these continuations made any sense so we try a different branch go over here and now we maybe output here it’s just one but we may be again output four of them we prune away three but one of them is actually good then we continue and so on so I hope you can see this is a very classic Tree Search right it’s a tree search you can do this this breath first or depth first um but it’s a tree search essentially with pruning an ordered tree search if y s um you have a thought generator that’s just generating one thought at a time so one intermediate step of the problem solving that you ask the language model based on the input and the previous thoughts you just say please make one intermediate step don’t solve the problem completely just wake make one little step and explicitly write down the result of that step that’s a thought in in Co in parlance um so they say that’s so we can either sample or propose those which means that we can go to th t they’re more constrained so you want diversity you don’t want stuff to repeat itself but in any case you generate Thoughts by sampling and then you have a state evaluator where you simply ask the model how good do you think that is on either way that’s value or vote please do you give it all the all the thoughts that have been output and you say which one of these is the best and then you count the votes with voting it might be a bit more tricky to do sort of the backtrack like to compare node ld go they would have some kind of Maximum steps that’s that’s fine um they would sort all the candidates they they have available if one of the candidates is above the value threshold they expanded so they go down down until no node like until all of these nodes in our example were like if all of that these nodes are red they say are no notice above the value threshold so we backtrack um this is it’s not a like a global expansion as I mentioned it I guess that would be a step further foreign so a linear sequence of things and therefore you might as well sample it at once and even the the self-consistency right here it’s just sampling multiple times in parallel whereas this thing the big difference is that you actively have to stop after each step um like sample three thoughts stop evaluate dot One Stop evaluate thought to stop evaluate thought three star off and then you have to decide which one is the best one and then from that point on you go to the language model again and say you t and as we’ll see that’s kind of the spirit of the experiments even though I think they they what they want to go for is like a general Problem Solver I I don’t think this goes into the direction of a general Problem Solver I think this goes into the direction of like including it into programming but the first um there are three tasks to evaluate on one is this game of 24. you have four numbers uh for example this these four numbers right here and you’re asked to come up with a mathematical ex generation and this here is the evaluation and yes you can you can reach like that you can get the language model to solve these things and you can probably do them better however however they um have to prompt it really specifically here in order to do that which is fine for research right but the the prompts are really like like you would program the algorithm except that the one part is really taken care of by the language model but the prompts are so specific to the problems that they almost hese algorithmic tasks and this here is like mini crosswords so you have a grid of five by five let’s do just do three by three for demonstration purposes and it’s all letters and it’s a crossword puzzle so you for example here you’d have a word um like let’s say you have ape ape okay and then here you have a clue like the life form I don’t know like like human ‘s are I’m I’m a terrible crossword Q generator or something like this um or like animal with long arms that lives in trees I don’t okay u think might be correct and you fill in another word and all of a sudden you realize ah that doesn’t work out do you kind of cross out the ones again that you previously filled in try some other ones this is extremely handy like in this problem like a backtracking tree search is extremely handy that’s why they evaluated on it they’re not shy of saying look we evaluate the things on the task where we think it’s going to benefit but that that also should tell you that this method um is probably g put the full puzzle at one point just intermediate ones and then at the end give me the result the tree of thought setup however is much more integral in intricate um so they use depth first with with tree of thoughts um keeps exploring the most promising subsequent word clue until this state is no longer promising then backtrack to the parent state to explore alternative thoughts that’s the DFS tree of thoughts algorithm they presented above they say okay to make search more tractable subsequen yeah so what I mean right here is that they help a lot right and in fact which isn’t bad and you know the only criticism I would have right here is that these these things all the oh we translated into letter constraints and so on um I guess it would be possible to help the Chain of Thought prompting like the Baseline uh a bit by doing that as well I feel like that should be possible and I feel like for a fair evaluation that should probably be attempted but in any case you can see they help a l right they say in the text hey our goal isn’t they say it’s something like this our goal we know they say we know that there are algorithms to solve this the goal is not just to solve the task as more General crosswords can be readily solved with specialized NLP pipelines that leverage large-scale retrieval instead of language models okay so the they say you know our we want to do a general Problem Solver that explores its own thoughts and guides its own Exploration with deliberate reasoning as bad but it does mean to me that the way I see this as I said is in programming so in programming I could have my code yada yada do this do that do this and then here instead of calling a function like f uh that function would not be somewhere in my code but that particular function would be sort of maybe a language model doing something somewhere and then I could Implement something like a DFS or so um and try to call that function as part of it but I don’t see this at the level yet of what I th ould have the one prompt and then the intermediate steps they should all be governed by one prompt right not by explicit prompts saying hey uh here is the here is the decoding constraints and so on now give thoughts about this and we parse it for you even with the math problems they like parse it intermittently and so on um none of that should happen it should just be one prompt that essentially generically says consider your previous thought you know how good do you think it is and so on um it this here is the crossword results you can see the i o prompting in Chain of Thought prompting they barely managed to solve full games they sometimes have word and letter successes but not that many the tree of thoughts uh obviously is much better and if you they say this is pretty cool they say hey if we heuristically if we Oracle we know which words go where right so if we always at valuation time so when the model criticizes itself and selects the best thought if at that time we always tell t backtrack and go to another Branch you can see that the performance it decorates again interestingly it doesn’t degrade too much in sort of the success rate it does degrade a bit but okay it does the great I guess I guess the numbers are fairly big but the real killer is it solves a lot less total games so the total games are kind of an indication of if you make an a mistake like somewhere then you might get a lot of the words correct but the total game won’t be solved um and so the total games ms mixed with language models I think that’s an interesting World let me know what you think that was it for me bye-bye”

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